Conference Agenda

Topographic mapping and surface motion estimation with spaceborne SAR sensors are the main topics of the Dragon-4 project "Multi-baseline SAR processing for 3D/4D reconstruction (id 32278-2)” under the framework of THREE- AND FOUR- DIMENSIONAL TOPOGRAPHIC MEASUREMENT AND VALIDATION (id 32278). In Dragon-4, we work on different test sites investigating the following topics:

1. Topographic mapping with SAR. Interferometric SAR (InSAR) is the main method for the generation of digital elevation models (DEM) with SAR observations. The StereoSAR-assisted InSAR topographic mapping strategy is presented in the following three steps. Firstly, the StereoSAR DEM can be employed as a reference to remove the main topographic phase from the InSAR interferogram, which can reduce the fringe frequency and facilitate the phase unwrapping of the InSAR interferogram. Then, the StereoSAR DEM can be used to calibrate the unwrapped phase of InSAR to determine the absolute phase deviation. Finally, the StereoSAR DEM and InSAR DEM are fused by weighted averaging, in which the determination of reasonable weights is the key issue to be solved. Thus the random height error can be effectively reduced and the StereoSAR DEM can also fill in the void in the original InSAR DEM. The effectiveness of the proposed methods was demonstrated by experimental results with high-resolution TerraSAR-X data pairs for the test site of Mount Song, one of the five sacred mountains in China.

2. Urban subsidence analysis.SAR systems can measure distances and movements with high precision. Using for example PS-InSAR, deformations can be estimated with a very high precision. The long-term surveillance of urban subsidence and the infrastructure stability in Shanghai is our major research goal since Dragon-1. With data starting from ERS-1, over ENVISAT ASAR, ALOS PALSAR, up to modern systems like TerraSAR-X, COSMO-SkyMed, PALSAR-2, and Sentinel-1, we continuously monitor the subsidence over Shanghai for far over a decade now. Furthermore, the subsidence distribution of Wuhan is derived from the long-term Sentinel-1 data stack. The InSAR-derived results also highlight active motions of built-up areas and infrastructures, such as some communities and railways segments. It can benefit safety screening and risk assessment. Furthermore, the Sentinel-1 data stacks are also applied in monitoring the infrastructures such as bridges.

Abstract - 3D SAR imaging by tomographic processing of multibaseline interferometric data has emerged for operational spaceborne monitoring of forest biomass in incoming or next ESA missions. However, a few open issues, or improvement needs, still stand, in particular of radiometric accuracy of the most diffused superresolution processing, the adaptive Capon method. After its introduction in SAR Tomography by University of Pisa a decade and a half ago, despite the widespread experimental use its basic structure, and issues, remained unchanged. In this work, an alternative improved (double) adaptive algorithm for spaceborne forest SAR Tomography is tested, characterized, and tuned by simulations, showing that it can furnish a better tomographic performance trade-off than Fourier and classic Capon Tomography. First corresponding low frequency real SAR data tests are also performed.

3D SAR Tomography (TomoSAR) [1-2] is a well established technique, for which operational interests related to spaceborne SAR missions have emerged, in particular for urban and forest applications. TomoSAR stems from advanced multibaseline (MB) Interferometry, exploiting the MB cross-track array typically constructed by multiple SAR passes for beamforming and steering along the vertical axis, estimating the 3D distribution of the backscattered power in volumetric scenarios. This is typically accomplished by spatial (baseline) spectral analysis [1,2], each scattering component at a given height originating a corresponding spatial frequency component in the MB data vector.

In particular, concerning the incoming ESA mission BIOMASS for forest monitoring, the well know, tested and widespread superresolution Capon method [2,3] is foreseen to be exploited, which in a simple adaptive and light burden manner is able to get a height resolution beyond the overall baseline-related Rayleigh limit, and reduce layer cross-talk i.e. height sidelobes especially for typically non perfectly uniform baselines. This, in parallel to Fourier TomoSAR, that offers limited layer resolution capability and sensible cross-talks. Unfortunately, beyond the attention given to long-term temporal decorrelation [4] affecting all the TomoSAR methods for forest applications that are not based on companion satellite concepts, it is well known that Capon TomoSAR is affected by radiometric issues, presenting in the practical applications, with limited number of looks and residual data miscalibration, power losses in the height-resolved backscatterers, resulting in a non-linear behaviour.

It is thus the goal of this work to tune and test an alternative adaptive method [5] for TomoSAR imaging, offering height superresolution and sidelobe cleaning with improved radiometric capabilities. Both simulated analyses will be developed of the 3D imaging quality and radiometric fidelity, and first low frequency real forest data tests carried out.

Insights in the Capon radiometric issues are first given. In particular, a source of the power losses resulting in the Capon non-linearities is the self-cancellation phenomenon intrinsic in the Capon concept. To get the height superresolution and cross-talk reduction, the Capon algorithm relies on the knowledge of the MB array response (steering vector) and of the spatial (baseline) correlation matrix, to adaptively reject the interfering scattering coming from height directions different from that currently targeted during the height scan [2,3].

In this process, deviations of the actual steering vector from the nominal one, related to residual miscalibrations typically after atmospheric compensation, and imperfect correlation estimates, lead Capon to misinterpret the data component from the targeted height as an unwanted interference to be reduced, so tending to cancel also the signal of interest, resulting in non-linear radiometric sensitivity. This can be only partially controlled by the well-known diagonal loading method, that tends to brake the critical adaptive interference rejection.

The method presented here to cure these issues of Capon TomoSAR is based on a specific preconditioning of the MB data before adaptive spectral estimation [5]. In particular, a pre-estimate of the current targeted component is (partially) compensated in the data to bypass the misinterpretation in the adaptive processing that triggers the self-cancellation. Two different partial compensation strategies are experimented in this work. The new TomoSAR method can be considered to be double adaptive, and advantageously trade-off superresolution, in particular the sub-Rayleigh resolution level, with the reduced radiometric losses i.e. improved linearity.

First simulated analyses are reported for a controlled characterization of the radiometric behaviour of the proposed method, with the Fourier beamforming and (loaded) Capon as comparison methods.

Typical realizations are shown of Tomo profiles for the new method and the reference algorithms. The MB array is composed by 6 almost uniformly spaced passes, which is a typical BIOMASS mission scenario, different residual phase miscalibration levels are applied, and the processed looks follows typical figures for the forest application. The backscattering scenario consists of two both equi and different power speckled sources, height-compact for an easier investigation, with typical forest SNR, and height separation slightly sub-Rayleigh. It is shown how the new method can offer a very good recovery of the expected peaks level, with amplitudes very close to the Fourier ones, overcoming the sensible Capon radiometric loss, still producing a well satisfactory superresolution and low sidelobes.

In the attempt to optimize the global tomographic performance trade-off, height accuracy is also analyzed and the partialization factor of the compensation step in the method tuned, finding an advantageous knee point. A more extended characterization of the Capon radiometric issues and of the new tuned method is also performed, producing sensitivity plots.

First real data trials are also performed of the new double adaptive method, for a line of low frequency airborne MB SAR data, taken over a forest. The proposed advance can be useful in the context of both the BIOMASS and the SAOCOM-CS programs.

[1] A. Reigber, A. Moreira, “First demonstration of airborne SAR tomography using multibaseline L-band data,” IEEE TGRS, 38(5), pp.2142–2152, 2000.

[2] F. Gini, F. Lombardini, and M. Montanari, “Layover solution in multibaseline SAR interferometry,” IEEE TAES, 38(4), pp.1344–1356, 2002.

[3] F. Lombardini, J. Ender, L. Rößing, et al., “Experiments of interferometric layover solution with the three-antenna airborne AER-II SAR system,” Proc. IGARSS 2004.

[4] F. Lombardini, F. Cai, “Temporal decorrelation-robust SAR tomography,” IEEE TGRS, 52(9), pp.5412-5421, 2014.

[5] F. Lombardini, F. Viviani, “Radiometrically robust superresolution tomography: first analyses,” Proc. IGARSS 2016.

Synthetic Aperture Radar (SAR) provides precise range and range-difference measurements. These measurements suffer from speckle noise, when there are more than one dominant scatterer in a resolution cell. However, when focusing on dominant and stable point-like scatterers, often called permanent scatterers (PS), the measurement of the backscattered signal is not affected by speckling and allows for precise measurement of distance differences using the interferometric phase differences. This is the reason for the importance of stable point-scatterers in SAR remote sensing, which are the base for techniques like PS-InSAR, but also for absolute position measurements with SAR geodesy.
Such point-scatterers can be found in rather large numbers in urban areas. The spatial density of these PS points depends on the structure of the area of interest, but also on the used wavelength, with shorter wavelengths providing a denser PS network.
In many areas outside of cities, there are only few stable point-scatterers to be found. Artificial targets, like corner-reflectors, can be an alternative solution for areas without ‘natural’ available point-scatterers. However, as corner-reflectors are large and expensive, they cannot be widely used outside of secured test areas, because they are prone to misuse and theft.
We propose the use of small, inexpensive artificial targets that can be used in large numbers under such circumstances. We demonstrate the use of such targets with TSX. For C-band in Sentinel, larger targets are necessary. We will demonstrate the possibility to also use cheap targets in C-band for co-pol and cross-pol cases.

Mapping forest height makes a great contribution to quantitative estimation of forest above ground biomass, leading to a better knowledge of carbon stocks stored in forests. In recent years, polarimetric SAR interferometry (Pol-InSAR) and SAR tomography (TomoSAR) techniques have become major tools for forest height retrieval based on SAR measurements. In polarimetric SAR interferometry, forest height is retrieved from single baseline polarimetric data, under the assumption of the random volume over ground (RVoG) model. For SAR tomography, instead, fully 3-D back-scattering profiles are reconstructed by jointly focusing data from multiple flights and forest height is then obtained by analyzing the shape of the vertical profiles. In this work, we aim at comparing these two techniques in the context of P-Band SAR retrieval of forest parameters in tropical areas. To accomplish this goal, both techniques are applied to the same SAR dataset at P-band, which is the one acquired by ONERA in French Guiana during the TropiSAR campaign. PolInSAR and TomoSAR forest height maps are then analyzed using Lidar measurements.

Forest above ground biomass (AGB) retrieval by P-band SAR Tomography has largely been studied in recent years, mostly in the frame of studies related to the forthcoming spaceborne Mission BIOMASS. Using SAR Tomography, it has been demonstrated that the backscattered power at the canopy layer is strongly correlated to the forest AGB. In the context of spaceborne missions, however, it is difficult to achieve enough passes for SAR tomography. Interferometric ground notching has recently been proposed as a new method to single out volume scattering contributions. The method takes as input a single pair of SAR images to obtain a ground-notched image by canceling out the backscattered power coming from the ground level. Most interestingly, the correlation between ground-notched intensity and forest AGB has been demonstrated to be very significantly improved w.r.t. the case of single images. In this paper, we evaluate the impact of temporal decorrelation on interferometric ground notching. A model is presented to show the impact of temporal decorrelation, and an experimental assessment is provided by analyzing data from the P-Band campaign BIOSAR-1, where multiple baselines where acquired both on the same day and with a time span of 23, 30, and 53 days. The experimental results show that ground-notched intensity is more stable for tall-forested areas, whereas the low vegetation is more affected by temporal decorrelation. Current work is ongoing to extend the analysis to tropical sites.

The monitoring of line-infrastructure, such as railways and dams, benefits from the synergy of SAR interferometry (InSAR) using multiple satellite missions. Different orbital and instrument viewing geometries, as well as spatial and temporal coverage and resolution, optimize the amount of information that can be extracted from the data. However, InSAR is an opportunistic approach as the location and occurrence of its measurements (coherent scatterers) cannot be guaranteed, and the quality of the InSAR products is not uniform. Therefore, advanced integrated products and generic performance assessment metrics are necessary. Here, we propose several new monitoring products and quality metrics for a-priori and a-posteriori performance assessment using multisensor InSAR, based on the assumption that:

1) coherent scatterers can be found representing the same physical phenomenon,

2) alignment of the datasets in space and time is possible, and

3) the influence of (nonperiodic) longitudinal movements compared to transversal and normal motion is limited.

The methods and metrics address two main operational questions.

1) Can we measure a particular deformation in a specific direction, at a specific location, and how well can we measure that?

Sensitivity values and Sensitivity circles are introduced, leading to a deformation variance as a function of the infrastructure orientation and the orthogonal elevation angle. The particular observability yields Minimal Detectable Deformations (MDDs) that can be observed with a given confidence level.

2) What can a particular combination of sensors produce as deformation products, and how does this compare with another combination of sensors?

We state the method for Line-of-Sight decomposition specifically to an asset-based coordinate system, and provide the variance covariance matrix in this coordinate system. The Dilution of Precision (DoP) is introduced as a scalar-valued quality metric, which is convenient to compare different sensor combinations.

Once InSAR data have been processed into deformation estimates, we introduce two operationally relevant end-products. First, the Significance Deformation Map (SDM) shows all locations on the selected asset where deformation is significant, given a confidence level as agreed with the asset-manager. Second, the Longitudinal Anomaly Profiles (LAPs) are a convenient way for instant information on the occurrence, location, and significance of anomalies along the track.

The proposed methods and metrics are demonstrated on a 125 km railway line-infrastructure asset in the Netherlands. All work contributes to a more structured, repeatable, and generic approach in the operational monitoring of line infrastructure.

This work has been recently published by the IEEE Journal of Selected Topics for Applied Remote Sensing, entitled ‘Monitoring line-infrastructure with multi-sensor SAR interferometry: products and performance assessment metrics’, doi: 10.1109/JSTARS.2018.2803074

Elevated highways, as one of the most important infrastructures, make contributions to a convenient and efficient public traffic, whose operational safety is the foundation of city development. Thus, deformation monitoring is the necessary prerequisite to normal operation of elevated roads. Persistent Scatterer SAR Interferometry (PSI) is a mature tool for land subsidence monitoring in an urban area, and its reliability has been verified by many studies.

In this research, we processed a long time-series of high-resolution TerraSAR-X satellite dataset in Shanghai from 2013 to 2017 to explore the spatio-temporal patterns along the elevated highways. Then with ground leveling data for InSAR accuracy verification, we compared and analyzed results between InSAR and leveling. The spatial distribution and temporal evolution of deformation characteristics of elevated highways were explored with joint analysis of PSI results, regional land subsidence, dynamic loads and the historical construction activities.

According to our results, regional land subsidence is a major factor for the deformation of Elevated highways because foundation of elevated highway is made up of end bearing pile foundation, which can generate frictional resistance between pile body and soil layer. The second factor we may consider is vehicle dynamic loads during the operational stage. We try to build a model between deformation and dynamic loads based on big data. And another one is the time when the elevated roads built. As is known, the land subsidence may undergo similar evolution after the engineering completion. So there are some relations between the completion time and deformation. Moreover, other factors such as groundwater, surrounding projects, etc. may play some but very small roles and we ignore them here.

Synthetic Aperture Radar (SAR) image co-registration is a fundamental but crucial procedure for interferometric SAR applications. Mainstream SAR coregistration algorithms are based on either cross-correlation or geometrical mapping. Both algorithms suffer from high computational expenses. The cross-correlation based coregistration, which is widely applied to conventional stripmap SAR data, requires many sub-image patches and an oversampling operation to derive the robust offsets with one tenth pixel accuracy. While the geometrical co-registration, which is widely applied in S-1 Interferometric Wide Swath (IW) images, is quite time consuming due to wide imaging coverage of TOPS mode and the iterative range-Doppler method. The massive parallelism of Graphic Processing Units (GPUs) can be used to improve the calculation efficiency of the two algorithms. The two new parallel algorithms are developed in NVIDIA’s Compute Unified Device Architecture (CUDA). The parallel cross-correlation based algorithm is implemented on batched processing for small matrices operation. The parallel geometrical processing is optimized by parallel pipelines. The efficiency improvement of the two parallel algorithms can be observed via the contrast experiments on Envisat stripmap and Sentinel-1 IW data.

Sentinel-1 Capability of Surface Deformation Estimation over a Wide Area in North-Eastern Algeria

Abstract

Monitoring ground deformation over a wide area with classical geodetic techniques, such as Geodetic Levelling and Vertical inclinometer, is very time consuming and expensive. On the other hand, interferometric synthetic aperture radar (InSAR) has been successfully used over the last two decades to produce high spatial density displacement maps in centimeter/millimeter accuracy with relatively low cost. InSAR gives the opportunity to study various phenomena, like fault creep, landslides, and subsidence induced by groundwater extraction.
This work is focusing on detecting land deformation and study geohazards such as landslide and subsidence over large areas in northeast Algeria by using Sentinel-1 data. Sentinel-1 data is provided by the Copernicus Program satellites constellation conducted by the ESA. Sentinel-1data is acquired in TOPS (Terrain Observation by Progressive Scans) mode, which is mainly designed for the purpose of wide coverage capability. We used all available data acquired over the study area between 2015 and 2018.
Stacks containing hundreds of SLC images covering the study area in ascending and descending orbits were analysed for land surface deformation using SarProZ and applying Persistent Scatterer Interferometry (PSI) technique.
This study main objectives are: first, increasing the detection capability of active landslides and ground subsidence; second, monitoring and analyzing the temporal evolution of the detected deformations by providing a time series deformation maps and velocities maps over a period of time. In the end, to understand deformations distribution and investigate geomorphological and geological causes.
Results revealed the characteristics of the study area where large areas of sparse vegetation and rocky surfaces, especially at high altitude and around the main mountainous structures, have a low density of measured PS points. On the other hand, areas where the vegetation cover decreases, especially in the urban areas, the density of Persistent Scatterers increases. Obtained deformation maps in these areas are composed of dense PS points. Where, the biggest amount of PSs is found in the urban area, the rest in the monotonous rocky areas.
A deformation time series was calculated for each Persistent Scatterer. Each point associated with the value of the annual linear velocity (mm/yr), estimated over the analyzed period and the displacement accumulated at each sensor acquisition date (mm). The measures are referred to the movement of the ground point in the satellite Line of Sight (LOS) direction.

Recently, the Sentinel-1 data has received extensive attention due to its large coverage and free availability. It is often used for monitoring of large-scale volcanoes and earthquakes, but it also offers an opportunity for subsidence monitoring in built-up areas although it is conventionally not included as high-resolution imagery.

The study area in Wuhan is along the Yangtze River, and has developed rapidly in recent years. Rapid urbanization and extensive carbonate rock strips as well as soft soil layers underground in Wuhan have contributed to land subsidence in most parts of Wuhan. The risks to the safety of buildings and public infrastructures are concerned by municipal departments and citizens. It is crucial to detect land subsidence to facilitate understanding of the evolutionary processes so that proper measures can be taken to carry out effective planning and construction and to mitigate further loss.

The time-series analysis method is adapted to derive the deformation from Sentinel-1 images. In this case, StaMPS is introduced for data processing, which does not need to assume a special deformation model, directly through the three-dimensional phase unwrapping algorithm to obtain surface deformation information ^[1]. It is used to extract deformation on constructions combining with high-resolution imagery ^[2]. Whereas application of Sentinel-1 data in monitoring deformation in built-up areas is relatively few.

In this experiment, totally 44 scenes of the IW mode data of the Sentinel-1A are collected. Using the free Sentinel-1 data, time-series PS-InSAR technology ^[3] is applied to obtain the deformation rate of Wuhan City and there are several areas with severe subsidence, with a maximum subsidence rate of -27mm/y. Furthermore, the InSAR-derived displacement map also highlights active motions of built-up areas and infrastructures, such as some communities and railways segments. It helps with safety screening and risks assessment.

An overall subsidence of -15mm/y to -27mm/y occurs in Anjuyuan Community, with a cumulative deformation of 30mm. From the obtained time-series curve of the PS points, the subsidence rate of the community accelerated from August 2016 to November 2016, during which the subsidence exceeded 10mm, which is equivalent to the subsidence in the previous 16 months. It should be paid more attention to.

Remarkable subsidence occurs in the Fazhanercun and Jingnan Community in Hankou district. The maximum subsidence rate is -27mm/y, and the maximum deformation is even -38mm. According to the survey, since the start of reconstruction project of the Village in the south of the community, the walls of some households in this community were cracked, and the front steps were severely separated from the ground, which is consistent with the experimental results, indicating that the processing results of Sentinel-1A dataset is reliable.

An analysis of a section of a railway passing through Hankou Railway Station shows that the majority railway of this section suffers from different degrees of subsidence, and the deformation rate in the vertical direction is between -11.64mm/y and 6.18mm/y. Besides, the differential subsidence in the railway curve is relatively large, and it is worthy of attention. The deformation rate in the vertical direction of a section of Wuhan-Guangzhou Railway passing through Wuhan Railway Station is approximately -5mm/y to 5mm/y. Except for a slight uplift near the Wuhan Railway station, this section is overall sinking slightly and there is no uneven subsidence.

REFERENCES

[1]. Hooper, A.J., Persistent scatter radar interferometry for crustal deformation studies and modeling of volcanic deformation [D]. Stanford University, 2006.

[2]. Qin. X, Liao. M, Yang. M, and Zhang. L. Monitoring structure health of urban bridges with advanced multi-temporal InSAR analysis [J]. Annals of GIS. 2017, 23(6):1-10.

[3]. FERRETTI A, PRATI C, ROCCA F. Permanent scatterers in SAR interferometry [J]. IEEE Transactions on Geoscience & Remote Sensing, 2001, 39: 8-20

Land reclamation is a well-known solution for land augmentation on coastal areas to serve the population increase, rapid urbanization, and economic development. Many countries have expanded its coastal land by using this method including England, Korea, Germany(Flemming and Nyandwi 1994), Ireland, Netherlands, Spain, Bangladesh, Nigeria, and China. Remote sensing has represented a high capability in detecting environmental changes caused by land reclamation in coastal areas due to its wide coverage, periodical revisit, and different data types and techniques. In this study, we have utilized microwave remote sensing data of Sentinel-1 to estimate the stability of reclaimed areas’ surface in Shenzhen City, Guangdong Province and Hong Kong, Southern China.

This study area located in the southeastern part of the Pearl River Estuary (PRE). The Pearl River is the third largest river in China and the second biggest river measured by mean annual runoff. The river’s water flows through eight different outlets into the South China Sea. Four of which are located in the north and west of the PRE. The coastal area of this area is under an intense pressure of land reclamation and almost can be considered as an artificial environment due to the recent economic development. This area has witnessed an enormous urban encroachment on agricultural land and coastal swamps during the last four decades due to the open-door policies in China since 1978. This urban expansion was a result of high population density and rapid industrialization in the Pearl River Delta since then.

Reclaimed areas in the study area were defined based on the land expansion on the estuary’s water in Shenzhen and Hong Kong coastal areas since 1973. The first available Landsat Multispectral Scanner System MMS images for the PRE which had been acquired in December 1973, were used to map the PRE water surface body in 1973 as the study’s area of interest (AOI). Then, the most recent images of Sentinel-2 were used to define the recent coastal line in our study area. Normalized Difference Water Index (NDWI) was applied to all images to extract the water body of our study area. This method based on the high reflectance of water in the green light wavelength and its low reflectance in the near-infrared wavelength.

After extracting reclaimed areas in the period between 1973 and 2018; Sentinel-1 data was used to detect surface deformation on these areas by applying PS-InSAR technique using SarProZ software. 65 Ascending images between June 2018 and January 2018 were utilized for this purpose.

Results revealed more PS points on the older impervious surfaces land cover of reclaimed areas compared to recent built-up and cultivated areas. The overall results showed stable surface in most reclaimed areas. However, some reclaimed areas represented surface deformation reached in some places to -30 mm/year such as areas southern Bao’an airport in Shenzhen City and some wave breakers in Hong Kong.

InSAR has proved a powerful technique for mapping surface deformation, developing rapidly in recent twenty years. But one-dimensional InSAR LOS measurement has limited its application to retrieve 3-D surface displacements, as it is only sensitive to surface movements towards or away from the satellite. The most straightforward approach is to integrate InSAR LOS measurements with homogenous data (Offset tracking, MAI) or heterogeneous data (GPS data, leveling). In this paper, we reconstruct the three-dimensional deformation field with surface-parallel flow assumption based on the knowledge of DEM information on ground deformation. In addition to, due to the different influence of the errors in different observation data, the iteration method by correcting characteristic value with maximum likelihood estimation is used to literately process the function model to get the accurate random model through prior information, and also the exact weight function. We apply this method on the Jiaju landslide and the results shows, horizontal displacement of Jiaju landslide appears to move along the landslide direction in the east-west direction, vertical deformation rate of the north part is large which exceeding -2cm/y, while the south part is -0.5cm/y.

Coastal environments and, in particular, ground motions in coastal areas are in practice often poorly known, and in many cases, only little information is available about the relevant patterns and time evolution. For this reason, it is strategic the continuous monitor of coastal delta regions by the use of advanced Earth Observation (EO) systems that are capable to detect and monitor the evolution of surface deformation phenomena, recovering their spatial extent on the ground and following their temporal variability. This is beneficial for the subsequent interpretation of natural/anthropogenic processes causing surface motion.

The activities performed within this present Dragon IV project have mostly been focused on the analysis of modification processes that characterize two important Delta river areas in China: the Yangtze and the Pearl River Deltas. Principally, we focused on Shanghai area but also a few experiments have been carried out on PRD. Both delta regions are significantly affected by sea-level rise and natural/anthropogenic deformation phenomena, making it clear the need of extended analyses for a better understanding of the mechanisms responsible for the observed surface modifications, and for the planning of actions devoted to risk prevention for populations living in coastal areas.

More specifically, the main aim is to retrieve long-term displacement time-series from EO data, specifically satellite Synthetic Aperture Radar (SAR), of the investigated areas through advanced differential interferometric synthetic aperture (DInSAR) techniques [1]-[2].

To evaluate the combined risk of sea level rise, storm surges, and ground subsidence, the availability of high-resolution digital elevation models (DEM) of monitored coastal areas has also resulted mandatory. Added-value EO data-products, such as the updated DEMs of coastal areas subject to sea-level rise, the time-series of terrain displacement, mean displacement velocity maps, and time-series of SAR backscattering maps, have been obtained by exploiting archives of SAR data at different spatial resolution spanning more than 10 years, from 2007 to 2018.

A few experiments have been conducted. In particular, the combined use of ENVISAT, Cosmo-SkyMed, and Sentinel-1 data have permitted to recover long-term displacement time-series of the ocean-reclaimed lands. New combination methods for the retrieval of the components of surface deformations from InSAR-driven LOS-projected measurements have been applied, and the most relevant results have been published on peer-reviewed journals [3]-[5].

Further investigations are currently being in progress to assess the risk of flooding of the coastal region of Shanghai, by benefiting from the retrieved InSAR deformation maps and a digital elevation model (DEM) of the area. The latter has been generated by using 2012 TanDEM-X bistatic SAR data [6]. The results of all these investigations will also be presented in separate communications at the mid-term D4 meeting.

We would like to remark that these studies are the result of a strict cooperation between the European and Chinese research institutions involved in the project.

Finally, some results evidencing the current ground deformation of the Pearl River Delta (PRD) region, obtained using Sentinel-1 acquisitions acquired over the last two years will be presented. In particular, we have selected this test-site area as a laboratory to evaluate the performance of a new multi-grid phase unwrapping approach. We moved from the observation that new-generation satellite data are characterized by larger spatial coverage and/or improved spatial resolutions, thus leading to augmented computational problems. In particular, the number of observation points in each SAR scene tends to considerably increase, thus posing new challenges. To overcome this problem, some multi-grid phase unwrapping methods, based on partitioning a scene in several overlapped, multi-resolution grids of pixels, and on their proper recombination [7]-[8], can be profitably adopted. In our experiments we focused on the PRD region and we provided InSAR-based analyses over multi-grids of pixels characterized by different spatial pixel spacing (i.e., from 500m x 500 m to 25m x 25m). Further developments consist in adaptively identifying the correct (most adequate) spatial spacing grid in each area, separately, depending on the observed spatial rate of deformation, so as to use finer grids in areas with significantly large rates of deformation and worse pixel spacing where deformation has a low spatial rate. Noteworthy, the efficient use of multiple grids of resolution can permit both to unwrap/process large interferograms (even on a continental basis) and, then, progressively “zoom in” given regions in conformity with the Nyquist sampling condition. Very preliminary results will be presented at the D4-meeting. A hybrid multi-scale experiment has also been performed on the Shanghai coastal area.

[1] Berardino, P., G. Fornaro, R. Lanari, E. Sansosti (2002), A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms, IEEE Trans. Geosci. Remote Sens., 40(11), 2375-2383.

[2] A. Ferretti, C. Prati, and F. Rocca (2001), Permanent scatterers in SAR interferometry, IEEE Trans. Geosci. Remote Sens., 39(1), 8-20.

[3] Zhao Q., Pepe A., Gao W., Lu Z., Bonano M., He M.L., Wang J., Tang X. (2015) A DInSAR investigation of the ground settlement time evolution of ocean-reclaimed lands in Shanghai, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 1763-1781.

[4] A. Pepe, M. Bonano, Q. Zhao, T. Yang, H. Wang, “The Use of C-/X-Band Time-Gapped SAR Data and Geotechnical Models for the Study of Shanghai’s Ocean-Reclaimed Lands through the SBAS-DInSAR Technique, “ Remote Sensing 2016, 8, 911; doi:10.3390/rs8110911.

[5] Lei Yu, Tianliang Yang, Qing Zhao, Min Liu and Antonio Pepe, “The 2015-2016 Ground Displacements of the Shanghai Coastal Area Inferred from a Combined COSMO-SkyMed/Sentinel-1 DInSAR Analysis,” Remote Sens. 2017, 9, 1194.

[6] Krieger G., Moreira A., Fiedler H., Hajnsek I., Werner M., Younis M., Zink M. (2007) TanDEM-X: A satellite formation for high resolution SAR interferometry. IEEE Tans. Geosci. Remote Sens., 45, 3317-3340.

[7] M. D. Pritt, “Multigrid phase unwrapping for interferometric SAR,” in IGARSS 95, Florence, Italy

[8] Antonio Pepe, L. D. Euillades, M. Manunta, R. Lanari: "New Advances of the Extended Minimum Cost Flow Phase Unwrapping Algorithm for SBAS-DInSAR Analysis at Full Spatial Resolution," IEEE Transaction on Geoscience and Remote Sensing, vol. 49, n° 10, October 2011, pp. 4062-4079.

Global mean sea-levels have risen during the 20^th century, and they will accelerate rising by up to ~60 cm by 2100 (Nicholls and Cazenave, 2010). However, the projections remain uncertain in estimating the rate of increase in melting of glaciers, Greenland and Antarctic ice sheets. The fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) suggests the higher values for sea level rise base on newer ice-sheet observations (IPCC, 2014). This will assuredly increasingly submerge risk in the low-lying areas of coastal zones throughout this century. Assessing and mapping the coastal inundation risk under sea level rise has been conducted in Charlestown, RI, USA (Grilli et al., 2017), Italian coastal plains (Antonioli et al., 2017), coastal zones of Poland (Paprotny et al., 2017), the Southeast Queensland (Mills et al., 2016), the Venice city of Italy (Sperotto et al., 2015), and Shanghai (Wang et al., 2012).

Moreover, non-climate-related anthropogenic processes, such as ground subsidence due to groundwater extraction, ground settlements due to large scale land-reclamation, and fast and non-linear subsidence phenomena of artificial sea wall, will exacerbate the risk to coastal zones and megacities and amplify local vulnerability. Making the situation worse is the combination of sea-level rise resulting from climate change, local sinking of land resulting from anthropogenic and natural hazards. Previous study has already stressed the significance of relative sea level rise in increasing coastal flood frequency (Karegar et al., 2017; Little et al., 2015; Cayan et al., 2008; Carminati et al., 2002; Shi et al., 2000).

The coastal vulnerability of mega-city, Shanghai, which is located at the Yangtze River Delta, is currently being amplified by the compounding effects of the time-dependent ground subsidence and the accelerated rate of sea level rise (Yin et al., 2013). The provided examples of delta regions affected by the combination of sea-level rise and significant modifications over time make clear the need of extended analyses for the understanding of the mechanismsat the base of the surface modifications of coastal areas, estimating of future regional relative sea level change, and evaluating the potential submerged land area. The main goals of this study are to provide a full characterization of the scene modifications over time and causes of the coastal region environments, to provide estimates of future regional sea level change, and to project coastal submerged area.

In this study, the use of well-established remote sensing technologies, based on the joint exploitation of multi-spectral information gathered at different spectral wavelengths, the advanced Interferometric Synthetic Aperture Radar (InSAR) techniques, and the hydrodynamic model-FloodMap projections will be employed for these purposes. The results obtained in this study represents an asset for the planning of present and future scientific activities devoted to the monitoring of such fragile environments. These analyses are essential to assess the factors that will continue to amplify the vulnerability of the low-elevation coastal zones.

In order to evaluate the combined risk of sea level rise and ground subsidence, the availability of high-resolution digital elevation models (DEM) of monitored coastal areas is generated with InSAR. The time-series of terrain deformation and mean deformation velocity maps, will be obtained by exploiting archives of Synthetic Aperture Radar (SAR) data with different levels of spatial resolution spanning a long time interval of about 10 years since the beginning of 2007 to 2017. SAR data will be collected by the former (i.e., the ESA ENVISAT-ASAR) and the new generation of radar sensors (the Cosmo-SkyMed constellations and Sentinel-1A). Large scale DInSAR analysis over wide areas will be performed by exploiting the Small BAseline Subset (SBAS) algorithm. The InSAR derived DEM with different spatial resolution and a 2D hydrodynamic model-FloodMap will be employed to investigate the evolving flood risk in the eastern coastal area of Shanghai and to derive coastal submerged area.

As global warming problem is becoming serious in recent decades, the global sea level is rising continuously. This will cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy. Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta, more vulnerable to sea level rise. Previous studies have shown that there is severe land subsidence in the reclamation area in the east of Shanghai [1-3]. Land subsidence greatly exacerbates the risk of sea level rise. How to obtain land subsidence data efficiently is crucial.

DInSAR technology can quickly obtain a large range of land subsidence information, and the accuracy can reach the millimeter level. Today, land subsidence monitoring using DInSAR technology has been widely used in coastal cities such as Shanghai, Guangzhou and Hong Kong [1-5]. However, limited by the satellite launch time and life cycle, it is difficult to obtain a long time series of land subsidence. Antonio et al. [2,3]. used the subsidence model derived from laboratory centrifuge tests and the Singular Value Decomposition (SVD) to joint the land subsidence time series of the three satellites of ENVISAT/ASAR, COSMO-SkyMed, and Sentinel-1A. In this way, a subsidence time series of up to ten years is obtained.

Based on this, we have found that the deviation of land subsidence time series obtained by using different joint strategies (Using a different order to ioint three satellite platform subsidence time series) at some high coherence points is larger. In this paper, By exploiting a set of 35 SAR images acquired by the ENVISAT/ASAR from February 2007 to May 2010 , a set of 61 SAR images acquires by the COSMO-SkyMed (CSK) sensors from December 2013 to March 2016, and a set of 33 SAR images acquires by the Sentinel-1A (S1A) sensors from December 2013 to March 2016, coherent point targets identified by using the Small Baseline Subset (SBAS) algorithm. Then, the subsidence time series of high coherence points was obtained.

We use the algorithm proposed in [1,2] to joint the subsidence time series of the three satellite platforms. We adopt different joint strategies: Strategy 1 is to first combine the subsidence time series of CSK and S1A, and then combine the CSK_S1A subsidence time series with the ENV subsidence time series; Strategy 2 is to first combine the subsidence time series of ENV and CSK, and then combine the ENV_CSK subsidence time series with the S1A subsidence time series. We set a threshold for the Euclidean distance of the subsidence time series obtained by the two joint strategies, and call the high-coherence point with a Euclidean distance greater than the threshold as "bad pixel" and the high-coherence point with a Euclidean distance less than the threshold as " Good pixel". Then, through the consistency matching algorithm of the un-joint subsidence time series of three satellite platforms between the bad point and the good point, the joint subsidence time series of bad pixels is corrected.

Meanwhile we use the monthly mean tide level series from Lvsi Station (1959 ~ 2011), and combine the Ensemble Empirical Mode Decomposition(EEMD), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Back Propagation (BP) Neural Network to propose two improved EEMD-GA-BP and EEMD-PSO-BP method for regional sea level change prediction. The use of GA and PSO optimize BP Neural Network can improve the accuracy, and PSO is superior to GA. Multi-platform long-term land subsidence time series and precise sea level predict time series provides a realistic meaning for the impact of relative sea level change on the coastal areas.

[1]Zhao Q, Pepe A, Gao W, et al. A DInSAR Investigation of the Ground Settlement Time Evolution of Ocean-Reclaimed Lands in Shanghai[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(4):1763-1781.

[2]Pepe A, Bonano M, Zhao Q, et al. The use of C-/X-band time-gapped SAR data and geotechnical models for the study of Shanghai's ocean-reclaimed lands through the SBAS-DInSAR technique[J]. Remote Sensing, 2016, 8(11):911.

[3]Yu L, Yang T, Zhao Q, et al. The 2015–2016 Ground Displacements of the Shanghai Coastal Area Inferred from a Combined COSMO-SkyMed/Sentinel-1 DInSAR Analysis[J]. Remote Sensing, 2017, 9(12):1194.

[4]Zhao Q, Lin H, Jiang L, et al. A Study of Ground Deformation in the Guangzhou Urban Area with Persistent Scatterer Interferometry[J]. Sensors, 2009, 9(1):503-18.

[5]Zhao Q, Lin H, Gao W, et al. InSAR detection of residual settlement of an ocean reclamation engineering project: a case study of Hong Kong International Airport[J]. Journal of Oceanography, 2011, 67(4):415-426.

Continuing investigations of tidal variability at multiple tide gauges in Hong Kong and the South China Sea (SCS) have identified correlations with the potential to amplify extreme water levels and nuisance flooding at certain locations. Observed changes are hypothesized to be due to mechanisms active on multiple spatial scales. The regional behaviour of the SCS may have changed tidal evolution via MSL rise, upper-ocean warming (and hence, stratification), or modulations in the baroclinic conversion at the entrance of the SCS (the Luzon Strait). The baroclinic tidal signal can be enhanced at the northern shelf of the SCS and can generate multiple PSI-type interactions that yield amplifications in minor tides such as M₃ that can be observed in Hong Kong. Additionally, the enclosed regions of Hong Kong have undergone massive land reclamation projects that may have changed the resonant and/or frictional response of the harbors to the regional dynamics. Previous works reported on the tidal anomaly correlations (TACs) to detrended MSL fluctuations, shown to be most important in harbour regions such as Victoria Harbor in Hong Kong. In this work, we highlight the intertidal correlations of diurnal (D₁) tides to semidiurnal (D₂) tides, which are positively reinforced through the northern SCS, and the correlations of overtide (OT) fluctuations to D₁ and D₂, shown to be negatively reinforced (i.e., anti-correlated) across the same region. The consideration of all water level variabilities may help explain the large TACs previously reported and may have serious implications for future water levels in Hong Kong.

We present an updated method for semi-empirical determination of significant wave height (H_s) estimation applied to Sentinel-1 SAR data. The method is based on existing semi-empirical algorithms, intending to identify a narrow-band swell-wave spectrum on the open-ocean waters and focused on linear imaging mechanism of tilt modulation on radar-looking directional surface wave. Utilizing the radar backscatter cross-section, we develop and evaluate a linear method to estimate H_s in various environment conditions without any prior knowledge from external input variables. Our method is divided into two components; first, the estimation of dominant wavelength using a 2-Dimensional Fast Fourier Transform (2D-FFT) and dimensionless coefficient, second, an estimation of ocean surface roughness and slope variation. The routines are aided by: adaptive filtering of the radar cross-section using median filters and Gaussian filters in different domains, linear fitting, and a detailed dependency analysis based on wave type and varying wind speed. Standard meteorological buoy data from the National Buoy Data Center (NDBC) is used for validation of the estimated Hs and input for the dependency analysis. These stations are selected to test differing water depths, wave properties, and wind conditions. This study employs Level-1 GRD Sentinel-1A and 1B SAR images from 2016 to 2017 covering locations of in-situ NDBC stations located near Hawaii, used for validation. Results show that the method performs well in estimating H_s under low to moderate wind forcing conditions (4 – 10 ms^-1) for any wave type in open-water areas. Lower performances are found under very low and strong wind conditions, and in wind-wave dominant environments.

This study focuses on the C-band radar backscatter features of the shallow water topography of Subei Bank in the Southern Yellow Sea using 25 ENVISAT (Environmental Satellite) ASAR (advanced synthetic aperture radar) and ERS-2 (European Remote-Sensing Satellite-2) SAR images acquired between 2006 and 2010. Under different sea states, SAR imagery shows different bathymetric features: the wide bright patterns with an average width of 6 km are shown under low to moderate wind speeds and correspond to sea surface imprints of tidal channels formed by two adjacent sand ridges, while the sand ridges appear as narrower (only 1 km wide), fingerlike, quasi-linear features on SAR imagery in high winds. Two possible SAR imaging mechanisms of coastal bathymetry are proposed in the case where the flow is parallel to the major axes of tidal channels or sand ridges. Two vortexes will converge at the central line of the tidal channel in the upper layer and form a convergent zone over the sea surface when the surface Ekman current is opposite to the mean tidal flow, therefore the tidal channels are shown as wide and bright stripes on SAR imagery. For the SAR imaging of sand ridges, all the SAR images were acquired at low tidal levels. In this case, the ocean surface waves are possibly broken up under strong winds when propagating from deep water to the shallower water, which leads to an increase of surface roughness over the sand ridges.

To satisfy the growing land demand for industrial and urban development, man-made lands, reclaimed from the sea, are used to build airports, harbors, and industrial areas. However, in such reclaimed areas, foundation settlements caused by unconsolidated soils are of public concern, and may induce severe damage to buildings and infrastructures. In such a context, Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) technique [1] is able to retrieve ground displacements, with centimeter to millimeter accuracy, by exploiting the phase difference between two SAR images acquired over the investigated area at different times and from different orbital positions.

Advanced DInSAR approaches, such as the Persistent Scatterer Interferometry (PSI) [2] and the Small BAseline Subset (SBAS) technique [3], nowadays represent effective tools for remotely detecting, mapping and monitoring surface deformation phenomena, thanks to their capability to produce spatially dense velocity maps as well as long-term displacement time-series corresponding to coherent targets location.

This study is focused on the retrieval of deformation signals over the ocean-reclaimed lands of Shanghai, China, and it is mostly devoted to the development of an ad-hoc procedure based on the combination of multiple-scale of resolution information. Over the last recent years, several investigations [4]-[5] have been carried out to study the deformation of the coastal area of Shanghai. In particular, the time evolution of ground deformation occurring over the coastal zone was derived from 2007 to 2017 [5] by jointly analyzing sequences of X-band (COSMO-SkyMed) and C-band (Sentinel-1A and ENVISAT/ASAR) SAR images. To achieve this task, a novel approach to link the time-gapped COSMO-SkyMed and ENVISAT/ASAR data was applied and an SVD-based combination approach to link time-overlapped COSMO-SkyMed and Sentinel-1A SAR data was developed. More precisely, the temporal evolution of the sensor-line-of-sight terrain deformation occurring over the coastal area of Shanghai was retrieved by independently processing the available sets of SAR images. This was done by analyzing sequences of multilooked differential SAR interferograms generated from the stacks of SAR images collected from 2007 to 2010 by the ENVISAT sensor, from 2013 to 2016 by the CSK sensors’ constellation, and from 2015 to 2016 by the Copernicus S1A radar instrument. The well-established Small BAseline Subset (SBAS) differential interferometry technique [3] was used for retrieving the LOS deformation time-series for each SAR sensor. Subsequently, for each coherent pixel found both in the ENVISAT and CSK datasets, the time-gapped ENVISAT and CSK LOS deformation time series were preliminarily converted in vertical (subsidence) deformation and, then, linked by using a time-dependent geotechnical centrifuge model.

Starting from December 2017 a new set of CSK data is available relevant to the same track used in the previous investigations; however (due to the lack of a regular planning of SAR acquisitions over the investigated area) there is a big lapse of more than one year between the old (from February 2014 to March 2016) and the new CSK SAR dataset. This leads us to the impractability of obtaining long-term SBAS deformation time-series by exclusively using CSK data. This drawback, at the same time, lead us the possibility to conduct an experiment based on the application of a hybrid multi-scale SBAS strategy. The idea is to focus on a group of highly coherent point-wise targets that preserve their coherence, also after one year, and to generate the CSK 2014-2018 surface displacement time-series related to those pixels. The applied advanced approach, originally presented in [6], for the identification of the highly-coherent point-wise scatterers and for the subsequent generation of the displacement time-series will be adopted. Subsequently, the achieved time-series will be compared with those obtained by linking the two independent (time-gapped) sequences of CSK data (similarly to what done in [5]) by the model adopted in [4]-[5] through the solution of a non-linear optimization problem based on the use of the Levenberg-Marquadt technique. The goal of this present investigation is to prove that, at least in correspondence to the highly coherent targets on the ground, the expected deformations behavior dictated by the used model is in general agreement with the achieved results. This finding is interesting to check the validity of the model used in our previous investigations [4]-[5]. The preliminarily results will be presented and discussed at the next Dragon-IV mid-term meeting.

[1] R. Bürgmann, P. A. Rosen, and E. J. Fielding, “Synthetic aperture radar interferometry to measure Earth’s surface topography and its deformation,” Annu. Rev. Earth Planet. Sci., vol. 28, no. 1, pp. 169–209, May 2000.

[2] A. Ferretti, C. Prati, and F. Rocca, “Permanent scatterers in SAR interferometry,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 1, pp. 8–20, Jan. 2001.

[3] P. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, “A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 11, pp. 2375–2383, Nov. 2002

[4] Q. Zhao, A. Pepe, W. Gao, Z. Lu, M. Bonano, M. He, X. Tang, “A DInSAR Investigation of the Ground Settlement Time Evolution of Ocean-Reclaimed Lands in Shanghai,” IEEE Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 4, pp. 1763-1781, April 2015.

[5] Y. Lei, Y. Tianliang, Qing Zhao, Min Liu and Antonio Pepe, “The 2015-2016 Ground Displacements of the Shanghai Coastal Area Inferred from a Combined COSMO-SkyMed/Sentinel-1 DInSAR Analysis,” Remote Sens. 2017, 9, 1194.

[6] Antonio Pepe, L. D. Euillades, M. Manunta, R. Lanari: "New Advances of the Extended Minimum Cost Flow Phase Unwrapping Algorithm for SBAS-DInSAR Analysis at Full Spatial Resolution," IEEE Transaction on Geoscience and Remote Sensing, vol. 49, n° 10, October 2011, pp. 4062-4079.

Abstract

Due to large-scale infrastructure construction and land reclamation, the problem of land subsidence in Shanghai is becoming more and more serious, which will have a major impact on urban public safety. Shanghai has established leveling networks and GPS networks to detect land subsidence, but due to cost constraints, the resolution is relatively insufficient (Amighpey et al. 2016). In recent years, InSAR technology has been widely used to monitor urban land subsidence due to its low cost and high precision (Sansosti et al. 2010; Hooper et al. 2012). Sentinel-1A data were available in the single-look-complex (SLC) format and acquired through the interferometric wide swath (IW) mode by employing the terrain observation by progressive scans (TOPS) acquisition mode, which provides large swath widths of 250 km at ground resolutions of 5 m x 20 m. In order to study the distribution and spatial pattern of land subsidence in Shanghai, a set of 33 SAR images acquired by the Sentinel-1A from July 2015 to August 2017 (ascending passes, VV polarization, with a side-looking angle of about 39˚and a satellite heading angle of about 348˚) were exploited to get coherent point targets as long as land subsidence velocity maps and time series which were identified by using the Small Baseline Subset (SBAS) algorithm (Berardino et al. 2002; Lanari et al. 2007). SBAS is based on the use of multiple-master multilook interferograms generated after a proper selection of Small Baseline (SB) SAR data pairs. LOS displacement time-series are computed by solving a least-squares (LS) minimization problem, based on the application of the singular value decomposition (SVD) method, to the sequence of unwrapped multilook interferograms. In this paper Sentinel-1A data were processed by the SBAS toolbox implemented within the commercial ENVI’s SARScape modules from EXELIS VIS Information Solutions with the coherence threshold of 0.35 and the maximum temporal baseline set to 180 days.

Urban land subsidence is mainly caused by infrastructure construction and groundwater extraction (Galloway et al. 2011; Wang et al. 2017). We use Landsat optical satellite data to analyze the changing trend of coastline in Shanghai since the 1990s (Landsat5、7、8, resolutions of 30m x 30m, every five years collected in winter). It is found that the coastline is expanding dramatically and most of the coastal areas with obvious settlement are the regions which has been reclaimed in the recent decade. In the central city, subsidence areas are densely located along subway lines. Since Shanghai began to reduce the exploitation of groundwater from the 1970s, the current settlement in Shanghai is mainly due to the unstable geological structure of the reclamation area and the construction of a large number of above ground and underground infrastructure in the city. By focusing on the land subsidence trend in high-rise buildings in urban areas and coastal areas, we found that the settlement in the coastal area is still significant, and the high-rise buildings in the area along the Huangpu River also have a subsidence of up to 2 cm/y.

Reference

Amighpey, M., & Arabi, S. (2016). Studying land subsidence in Yazd province, Iran, by integration of InSAR and levelling measurements. Remote Sensing Applications: Society and Environment, 4, 1-8. doi: 10.1016/j.rsase.2016.04.001

Sansosti, E., Casu, F., Manzo, M. & R, L., 2010. Space-borne radar interferometry techniques for the generation of deformation time series: an advanced tool for earth’s surface displacement analysis, Geophys. Res.Lett., 37, L20305, doi:10.1029/2010GL044379.

Hooper, A., Bekaert, D., Spaans, K. & Arikan, M., 2012. Recent advances in SAR interferometry time series analysis for measuring crustal deformation, Tectonophysics, 514-517, 1–13.

Berardino, P.; Fornaro, G.; Lanari, R.; Sansosti, E. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2375–2383.

Lanari, R.; Casu, F.; Manzo, M.; Zeni, G.; Berardino, P.; Manunta, M.; Pepe, A. An overview of the small baseline subset algorithm: A DInSAR technique for surface deformation analysis. Pure Appl. Geophys. 2007,164, 637–661.

Galloway, D. L., & Burbey, T. J. (2011). Review: Regional land subsidence accompanying groundwater extraction. Hydrogeology Journal, 19(8), 1459-1486. doi: 10.1007/ s10040-011-0775-5

Wang, H., Feng, G., Xu, B., Yu, Y., Li, Z., Du, Y., & Zhu, J. (2017). Deriving Spatio-Temporal Development of Ground Subsidence Due to Subway Construction and Operation in Delta Regions with PS-InSAR Data: A Case Study in Guangzhou, China. Remote Sensing, 9(10). doi: 10.3390/rs9101004

In this study, the possible submergence area of the Yangtze River Delta (YRD) under the background of sea level rise is investigated combining both satellite data and numerical models. The sea level rises (SLRs) in the East China sea in the middle and end of 21^st century are first predicted based on the statistical analysis of historical satellite altimeter data. The mean SLR values in 2050 and 2100 are 20 cm and 35cm, respectively. Then a regional tidal wave model of the East China sea is constructed using the Finite-Volume, primitive equation Community Ocean Model (FVCOM), and a new storm surge inundation model of the YRD is developed (here we take typhoons Fung-wong and Wipha as examples) to analyze the possible submergence area. The results show that if there is no coastal protection, the maximum possible inundation caused by SLR through tidal wave propagation in 2100 is 2.5×10³ km², 87.2% larger than that at the current sea level, and the maximum submergence area during the two storm surges is 8.3×10²~2.7×10³ km². Considering a 4 m-high breakwater along the coastlines, there is no submergence in the above cases under the SLR in 2100, while the inundation is about 15.4 km² during typhoon Wipha when the breakwater is 2 meter high. The submergence mainly occurs in Jiangsu Province, especially Yan Cheng and Lian Yungang cities. It is suggested that the height of the breakwater should be not less than 2 m considering the impact of sea level rise and storm surges.

In the framework of the DRAGON4 Project, the National Institute of Geophysics and Volcanology of Rome (INGV, Italy) and the Northeastern University of Shenyang (China) collaborate to study the surface movement over several industrial regions in Northeast China. The traditional heavy industrial base of Northeast China, especially in the Benxi-Anshan-Shenyang-Fushun (BASF) region, has played an important role in the economic development of the region, although severe consequences on the local environment are taking place due to the continuous mining activities. Various geohazards, such as subsidence, landslides, ground breakage and building inclinations, have been threatening the safety of local people and the environment for decades. The continuous monitoring of the effects of the mentioned geohazards is thus of great importance for the local population well-being. The main objectives of the study are: to take advantage of the availability of dense remote sensing data sets in order to analyze the geohazards and their environmental impacts in the region; and then forecast when and how these geohazards might occur in the future and provide technical support for disaster prevention and damage reduction.

Time series InSAR, as a general term of a variety of algorithms, is able to analyze the spatial and temporal deformation over large areas. With a single SAR image data stack only deformation along the line-of-sight direction could be analyzed. In this analysis we use time-series InSAR results from multiple stacks (from ascending and descending orbits) to monitor slow motions gravitational deformations.

As an important type of glacier, mountain glaciers are often regarded as sensitive recorders and indicators of global climate change. Additionally, glacier movement, one of the most important features of glaciers, can cause serious natural disasters, such as debris flow and glacial lake outburst floods, that threaten human production and life. Thus, monitoring glacier movement has great significance for predicting glacial flow and hazards. COSMO-SkyMed is a constellation of four X-band high-resolution radar satellites with a minimum revisit period of 12 hours. Based on ascending and descending COSMO-SkyMed data acquired at nearly the same time, the surface velocity of the Yiga Glacier, located in the Jiali County, Tibet, China, is estimated in four directions using an offset tracking technique during the periods of 16 January to 3 February 2017 and 1 February to 19 February 2017. Through the geometrical relationships between the measurements and the SAR images, the least square method is used to retrieve the 3D components of the glacier surface velocity in the eastward, northward and upward directions. Four conclusions can be drawn. First, by applying the offset tracking technique to the intensity information of ascending and descending passes of SAR images and combining the four measurements with different directions, the 3D velocity field of glaciers can be estimated. Second, as a constellation with four radar satellites, COSMO-SkyMed has a short revisit time that can acquire ascending and descending images with very similar time periods. This technique has great potential to validate the true 3D velocity of glaciers using different image pairs mapping the same deformation field. Third, the Yiga Glacier had a stable velocity during the observation period from 16 January to 19 February 2017. The distribution of the glacier surface velocity is related to the elevation change. A maximum velocity of approximately 2.4 m/d is observed in the middle part of the glacier because the steepest slope is located there. With steadily decreasing elevation, the velocity in the upper middle and the lower middle portions of the Yiga Glacier stabilizes at approximately 40 cm/d. Finally, the low RMSE in the non-ice region indicates that the results are reliable.

As the third largest country with coal reserves, but China is the largest coal product and consume country in the word. The goafs formed by underground coal extraction often bring severe damages and geohazards to coal mining areas, characterized by uncertainty, slowly and unpredictable over a relatively long time period after post-mining. Generally speaking, detecting the spatial distribution of surface deformation caused by underground goafs for effectively is the basic work for response the subsidence control and geohazards assessment. Compared to traditional geophysical techniques, the satellite-based imagery geodetic observations, such as Differential SAR Interferometry (InSAR) technique, has been considerated as a powerful tool for potentially large-spatial coverage deformation monitoring of the earth’s surface with an accuracy within centimeters to millimeters. As a typical city which abundant in coal resource and thus developed, Xuzhou city, has been experiencing a large-scale and high-intensity coal mining activities over past more than a century, causing large-area land subsidence even collapse phenomenon.In this study, the Multi-Temporal InSAR analysis techniques that both Persistent Scatterers Interferometry (PSI) and Small BAseline Subset (SBAS) methods is implemented, to investigate and analyze the land subsidence over the Xuzhou region, and to conduct an in-depth assessment about the stability of several interested underground goafs, using 62 SAR imagery acquired by Copernicus’ Sentinel-1A satellite spanning July 2015 until Apir 2018. The maps of annal-average subsidence velocity and displacement time-series were generated. And, the reliability of the monitoring results was cross-verified by comparing the PSI results to the SBAS method and highlight the differences. The MT-InSAR results reveal that there have four significant subsidence areas in the Xuzhou region, mainly locating in Tongshan-Quanshan District, Jiawang District, Fengxian and Peixian Country, the main driving factor of land subsidence is underground coal-mining except Fengxian Country, and the most server subsidence take place in Tongshan-Quanshan District where the maximum subsidence rate about 48.6mm/a, which keep better consistency with coal-mining borde in the spatial pattern. Moreover, aiming to the several typical underground goafs, the subsidence characteristics of their were analyzed, and we found that the trend of subsidence over underground goafs present a remarkable behavior that the stabilize continuously in temporal and the subsidece area is shrinking in spatial.It is self-evident thatdetected displacement time series over underground goafs provide valuable insight into the spatial and temporal evolution of corresponding deformation phenomena in recent years, thus it contribute to offer essential insight to the long-term stability assessment of the subsidence coal-mining induced of the Xuzhou region.

Abstract:For a long time, monitoring of mining subsidence requires a lot of money and time, besides monitoring and prediction can not be effectively integrated.So in this paper, the mining area is monitored by using time series InSar, then the data of experimental results and support vector regression (SVR) are combined to predict the dynamic change of mining subsidence.Finally, the rapid monitoring of mine deformation,integration of monitoring and prediction are realized.Firstly,we use PS-InSar and SBAS-InSar technology to get the subsidence scope and development trend of mining area,then the monitoring results after weighted assessment are used as training ang learning samples of SVR algorithm to establish prediction function;Finally, by using the established prediction function, the rolling prediction is carried out based on the results of regression analysis.To test the proposed method,We taking Xinjiang sulphur gully coal mine as an example,and use 36 Scenes of sentinel-1 imagery from 2015 to 2017 to carry out experimental research and analysis.The result shows that：Time series Insar can well monitor the subsidence scope and development trend of mining area,Further more the result of the prediction error of mining subsidence is also better.The experimental results show the feasibility of the method.

Key words: Time series Insar.;Subsidence prediction; mining subsidence; SVR

The Zhouqu–Wudu segment of the Bailongjiang Basin in Northwest of China with a total area of 8917 km² lies in the middle south of the west wing of Qinling orogen. It is controlled by Qinghai–Tibet tectonic belt and Wudu arc structure, and affected by unlift of the Qinghai–Tibet plateau. This segment is located in the Qinling Mountains, and is surrounded by the Qinghai–Tibet Plateau, the Loess Plateau and the Sichuan Basin as the three major geomorphic units. Because of its geophysical conditions, the Bailongjiang Basin is one of the most severely landslide affected regions in China. More than 2000 medium and large landslides have been reported within the Wudu and Zhouqu segment. In this paper, 50 newly launched Sentinel-1 scenes from November 2014 to September 2016 are gathered, and a preprocessing chain of TOPS with SBAS-InSAR are generated to obtain the time series deformation, the active area within the five typical giant fossil landslides in the study area were detected, the maximum deformation and the average deformation were verified by field investigation and the displacement monitor measurements in the local landslide early warning system.

Multitemporal InSAR observations have proved to be key observations to characterize spatial and temporal variations of interseismic strain along major faults, allowing not only to retreive average interseismic velocity maps but also transient aseismic slip events, giving new lights on seismic hazard assessment. With their high temporal resolution and wide spatial coverage, Sentinel-1 (S1) InSAR data can be analyzed in time series to tackle the multi-scale issues of seismic hazard, as well as the off-fault deformation and non tectonic signals (such as seasonal hydrological loads) quantification.

We focus here primarily on the eastern border of the Tibetan plateau, from the Himalayan syntax in the south to the Ordos in the north, marked by major faults, recently broken (Longmen Shan thrusts at the origin of the Mw 7.9, Wenchuan earthquake in 2008) or known as seismic gaps unbroken for several hundred years. Geodetic data available to date (GPS, InSAR ERS / Envisat time series) show that some of these gaps are the site of aseismic slow slips (such as some segments along the Haiyuan and Xian Shui He faults, and possibly along the Himalayan front in Bhutan), which, depending on their spatio-temporal characteristics, can help to reduce the seismic hazard on these faults or, conversely, facilitate the initiation of future major ruptures. In addition, the eastern and southern borders of the tibetan plateau are marked by high mountain ranges (Longmen Shan in the east and Himalayas in the south, with elevations variations of several kilometers), subject to erosion and contrasting with basins affected by hydrological loads varying seasonally. We review here our most recent studies over this eastern border of the tibetan plateau, analyzing large scale velocity fields obtained from S1 data time series analysis over descending and ascending orbits, emphasing improvments in InSAR processing specific to S1 data.

The ~1000-km-long Chaman fault system consists of a series of subparallel left-lateral strike-slip faults and thrusts that form the transform to transpressive plate boundary between the Indian Plate and the Eurasian Plate. Studies based on geological and plate closure estimates show that the northward plate motion of India with respect to Eurasia is on the order of 35 mm/yr. Previous InSAR studies (Barnhart, 2016; Fattahi & Amelung, 2016) along the Chaman fault system based on Envisat and ALOS data have shown that the northeast-southwest trending Chaman fault itself only accounts for ~30% of this relative motion. How the remaining 70% is distributed or localized on adjacent structures remain to be determined. Such studies also revealed the existence of shallow creep along more than 300 km of the Chaman fault. The large spatial coverage of the recent Sentinel-1 data allows to tackle both the large-scale strain partitioning issue and the fault-scale creep behavior characterization.

In order to estimate strain accumulation rates along and across the Chaman fault system, we map the present-day interseismic velocity fields using long-swath (> 1250 km) Sentinel-1 (S1) TOPS radar images acquired on both ascending (T42, T71, T144) and descending (T151, T78) orbits, along the western boundary of the Indian subcontinent. Using an automatized processing workflow, we have processed time series of ~150 S1 images acquired between 2014 and 2018. Preliminary results show left-lateral shear velocities of ~20 mm/yr across the distributed plate boundary, with a complex partitioning between the main Chaman left-lateral fault, other adjacent left-lateral faults or secondary structures within the thrust belt. While ascending data are mostly sensitive to the left-lateral component of slip and vertical motion along the Chaman fault, descending data highlight horizontal and vertical motion across secondary structures branching on the main Chaman fault. Surface aseismic creep rate along the Chaman fault seems to reach up to ~10 mm/year and may extend along a ~600 km-long segment, between 28.5 ^oN and 32.5^oN, which appears significantly (50%) longer than that reported in previous studies. Surface creep thus accommodates ~30% of the tectonic loading along a significant portion of this plate boundary. Further data analysis and modelling will provide a better quantification of the creep rate amplitude and depth along fault strike, deep tectonic loading, and strain partitioning on secondary structures.

In the past 10 years after the Wenchuan earthquake, important information was obtained from analysis of InSAR data of the event, including fault geometry, slip-distribution, rupture propagation and dynamics etc. Though some GPS data collected over both sides of the earthquake fault, InSAR data with full coverage of the Sichuan basin and Longmenshan provides crucial information about the kinematic and dynamic processes of the earthquake.

For this particular region with drastic elevation changes, two end-member models were proposed to interpret the deformation mechanism of the Tibetan Plateau and generation of the Wenchuan earthquake, namely the thrust-fold belt or the viscous lower crustal flow models. Here we re-analyze the PALSAR InSAR data acquired ~10 years ago when we published the first results in both Sun et al. (2008) and Shen et al. (2009). A number of correction techniques we developed in these years are applied to the data, particularly for the ionospheric noise in meter scale. Hence the coseismic deformation is greatly improved in this analysis. Then we use a nonlinear-linear-mixed technique to invert the data for detailed rupture features of the Wenchuan earthquake. Our inversion indicates that a shallowly west-dipping segment to the south and a near-vertical segment to the northeast are good enough for fitting the InSAR data, and the displacements of a horizontal detachment extending to the west are not needed.

Thanks to ESA’s Sentinel-1 A/B satellites, a high-temporal resolution dataset over the Longmenshan region is now available, with the earliest acquisition in the middle of 2014. By using two advanced time-series analysis techniques on the TOPS mode data in both ascending and descending pass, we analyzed the deformation process on subswath-by-subwath basis covering the Longmenshan and Minshan regions, where the Wenchuan earthquake occurred in 2008 and the Jiuzhaigou earthquake happened last year respectively. Our first result indicates that the Sentinel-1 SAR dataset is promising for resolving the subtle tectonic deformation process of this region, though other sensors, such as ERS/Envisat, may suffer from heavy decorrelation in the same region.

A single Interferometric Synthetic Aperture Radar (InSAR) interferogram provides a measurement of ground movement with centimetric accuracy, and therefore can only detect large ground motions such as those caused by co-seismic slip or volcano eruption. For detecting small amplitude and long term displacement such as post/inter seismic motion or ground subsidence, a time series of interferograms is needed to overcome the errors resulting from the atmosphere, DEM and orbit. In most of the currently available InSAR time series analysis packages, two fundamental assumptions are made, namely that (i) deformation signals are correlated in time, and (ii) atmospheric effects are correlated in space but not in time. Unfortunately, since atmospheric effects can be highly correlated with topography, the second assumption does not hold in most cases. The temporal correlation of atmospheric delays may completely mask or bias the geophysical signals and introduce unpredictable uncertainties on the velocity estimates.

To overcome this, we propose a strategy which (i) employs a generic InSAR atmospheric correction model for each interferogram by using tightly integrated HRES-ECMWF grid model output and GPS ZTD pointwise observations (global and all-time useable in near real-time); (ii) utilizes a series of model performance indicators to identify the date(s) with poor correction performance, including cross validation of ECMWF and GPS ZTD values, observed phase and modelled atmospheric delay correlations and phase standard deviations; (iii) uses an atmospheric phase screening (APS) model using partially corrected interferograms from step (i) to estimate atmospheric delays for each interferogram: higher performance of the correction model and reliable performance indicators will improve the estimation of APS; and (iv) applies the conventional time series analysis approach to extract the mean deformation rate as well as displacement time series. Our experiments with the proposed method suggest it is particularly beneficial for InSAR time series over mountain areas, as the residual atmospheric errors after correction are more likely to be randomly temporally distributed, which allows an easier minimization through time series analysis.

Landslide is one of the major and most frequently occurring geo-hazards around the world. After the 2008 Wenchuan Earthquake in China, a series of large-scale landslides were triggered. Unexpectedness and concealed nature of the landslides significantly increase the destruction degree and difficulty to prevent, exposing people’s livelihoods and infrastructure at risk.

Space borne radar remote sensing could realize macro dynamic monitoring of large-scale landslide hazards and provide an efficient way to obtain landslide surface deformation and spatio-temporal characteristics, hence contribute to early detection and early warning for local disaster risk management. This work shares several radar remote sensing applications in multiple landslide monitoring case studies in Sichuan since 2014 to till date. Long deformation evolutions of these landslides could be retrieved from time series InSAR processing with joint use of multi-platform InSAR observations. To fully investigate and validate the great potential of Sentinel-1 on landslide monitoring in complex terrain mountainous areas, and integrate the radar datasets from Sentinel-1 and TerraSAR-X, this work realized the landslide surface deformation acquisition with multi scales, short time intervals, and long time series, which also verify the great advantage of multi-platform spaceborne radar remote sensing on landslide monitoring. What’s more, combined with in situ measurements and other remote sensing observations for subsequent analysis and validation, space borne radar remote sensing applications could demonstrate great potentials to identify the spatio-temporal characteristics and investigate the failure mechanism for hazardous landslides.

This paper concludes that a comprehensive and effective Earth Observation (EO) based local landslides monitoring could avoid future human and infrastructure loses in the hill and High Mountain regions around the world.

Previous work with TerraSAR-X [1,2] indicated that landslides can be monitored on steep densely vegetated slopes in hilly terrain using sub-pixel offset tracking, sPOT over the Shuping area, Hubei, PR China. In this work, Cosmo-Skymed Spotlight data is employed at a later time period (27 June 2016 to 30 August 2016) to assess whether the mitigation measures employed to prevent further landslip have been effective using both dInSAR and sPOT processing. The results show good agreement between both methods over this 3 month time period with a small progressive motion towards the NNW of magnitude 10cm in azimuth and 5cm in slant-range. This is much smaller than the previous (accumulated) motion of up to 1m/year from February 2015-2016 using SBAS offset tracking [2] and from February 2009–April 2010 and January 2012–February 2013 using sub-pixel offset tracking [1], prior to the mitigation methods. Part of the reason for the success of dInSAR which was next to impossible to apply previously was that the mitigation measures resulted in a substantial portion of bare earth which had much higher phase coherence than the previously vegetated area. A comparison of the three methods are discussed alongside which one is best in different circumstances.

This work was partially supported by the CSC and UCL MAPS Dean prize through a PhD studentship at UCL-MSSL. We thank Space Catapult, Harwell space campus in general and Terri Freemantle, in particular, for arranging the provision of Cosmo-SkyMed data through the CORSAIR010 data grant.

[1] L. Sun and J.-P. Muller, “Evaluation of the Use of Sub-Pixel Offset Tracking Techniques to Monitor Landslides in Densely Vegetated Steeply Sloped Areas,” Remote Sensing, 8, 25, doi: 10.3390/rs8080659

[2] L. Sun, J.-P. Muller, and J. Chen, “Time Series Analysis of Very Slow Landslides in the Three Gorges Region through Small Baseline SAR Offset Tracking ,” Remote Sensing, 9, 1314. doi: 10.3390/rs9121314

3D SAR Tomography (TomoSAR) [1-4] and 4D SAR Differential Tomography (Diff-TomoSAR) [8-14] can be used to exploit multi-baseline SAR data stacks to create an important new innovation of SAR Interferometry, to sense complex scenes with multiple scatterers mapped into the same SAR cell. In addition to the 3-D shape reconstruction and deformation solution in complex urban/infrastructure areas [2,4], and recent cryospheric ice investigations [5], emerging tomographic remote sensing applications include forest scenarios [3,6,7], e.g. tree height and biomass estimation, sub-canopy topographic mapping, and even search, rescue and surveillance. However, these scenes are often characterized by temporal decorrelation of scatterers, orbital, tropospheric and ionospheric phase distortion and an open issue regarding possible height blurring and accuracy losses for TomoSAR applications particularly in densely vegetated mountainous rural areas. Thus, it is important to extend characterisations of temporal decorrelation, orbital, tropospheric and ionospheric phase distortion.

We report here on 3D imaging (especially of vertical layers) over densely vegetated mountainous rural areas using 3-D SAR imaging (SAR tomography) derived from data stacks of X-band COSMO-SkyMed Spotlight and L band ALOS-1 PALSAR data stacks over Dujiangyan Dam, Sichuan, China. The new TanDEM-X 12m DEM is being used to assist co - registration of all the data stacks first and has raised a number of unforeseen challenges, which will be described. Then, atmospheric correction is assessed using weather model data such as ERA-I and compared against GACOS in addition to ionospheric correction methods to remove ionospheric delay. Then the new TomoSAR method with the TanDEM-X 12m DEM is described to obtain the number of scatterers inside each pixel, the scattering amplitude and phase of each scatterer and finally extract tomograms (imaging), their 3D positions and motion parameters (deformation). A progress report will be shown on these different aspects.

This work is partially supported by the CSC and UCL MAPS Dean prize through a PhD studentship at UCL-MSSL.

[1] A. Reigber, A. Moreira, “First Demonstration of Airborne SAR Tomography using Multibaseline L-band Data,” IEEE TGARS, 38(5), pp.2142-2152, 2000.

[2] G. Fornaro, F. Serafino, F. Soldovieri, “Three Dimensional Focusing With Multipass SAR Data,” IEEE TGARS, 41(3), pp. 507-517, 2003.

[3] M. Nannini, R. Scheiber, R. Horn, “Imaging of Targets Beneath Foliage with SAR Tomography,” EUSAR’2008.

[4] F. Lombardini, F. Cai, D. Pasculli, “Spaceborne 3-D SAR Tomography for Analyzing Garbled Urban Scenarios: Single-look Superresolution Advances and Experiments," IEEE JSTARS, 6(2), pp.960-968, 2013.

[5] L. Ferro-Famil, C. Leconte, F. Boutet, X. Phan, M. Gay, Y. Durand, “PoSAR: A VHR Tomographic GB-SAR System Application to Snow Cover 3-D Imaging at X and Ku Bands,” EuRAD’12.

[6] F. Lombardini, F. Cai, “3D Tomographic and Differential Tomographic Response to Partially Coherent Scenes,” IGARSS’08.

[7] M. Pardini, K. Papathanassiou, “Robust Estimation of the Vertical Structure of Forest with Coherence Tomography,” ESA PolInSAR ’11 Workshop.

[8] F. Lombardini, F. Cai, “Evolutions of Diff-Tomo for Sensing Subcanopy Deformations and Height-varying Temporal Coherence,” ESA Fringe’11 Workshop.

[9] F. Lombardini, “Differential Tomography: A New Framework for SAR Interferometry”, IEEE TGARS, 43(1), pp.37-44, 2005.

[10] Xiang, Zhu Xiao, and Richard Bamler. "Compressive sensing for high resolution differential SAR tomography-the SL1MMER algorithm." In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, pp. 17-20. IEEE, 2010.

[11] F. Lombardini, M. Pardini, “Superresolution Differential Tomography: Experiments on Identification of Multiple Scatterers in Spaceborne SAR Data,” IEEE TGARS, 50(4), pp.1117-1129, 2012.

[12] F. Lombardini, F. Viviani, F. Cai, F. Dini, “Forest Temporal Decorrelation: 3D Analyses and Processing in the Diff-Tomo Framework,” IGARSS’13.

[13] Tebaldini, S., & Rocca, F. (2012). Multibaseline polarimetric SAR tomography of a boreal forest at P-and L-bands. IEEE Transactions on Geoscience and Remote Sensing, 50(1), 232-246.

[14] Huang, Y., Ferro-Famil, L., & Reigber, A. (2012). Under-foliage object imaging using SAR tomography and polarimetric spectral estimators. IEEE transactions on geoscience and remote sensing, 50(6), 2213-2225.

On 3 September 2017, North Korea (Democratic People's Republic of Korea, DPRK) claimed it has successfully tested a hydrogen bomb that could be loaded on to a long-range missile. Seismic readings of 6.3 indicated the test was bigger than any other that has been conducted. Punggye-ri Nuclear Test Site is the only known nuclear test site of North Korea. During the past 12 years, nuclear tests were conducted at the site in October 2006, May 2009, February 2013, January 2016, September 2016, and September 2017. Because of political and other complex factors, it is impossible to obtain any GPS, geology, and field surveying data for direct monitoring and research. InSAR provides a new inspiring research method for underground nuclear deformation monitoring. Here, we use multiple spaceborne SAR data that are ALOS-2, Sentinel-1 and TerraSAR-X to retrieve surface displacement caused by the latest 3 events. The results show that InSAR provides an independent tool to locate and retrieve surface displacement of nuclear tests in North Korea as a supplement of seismic and other methods.

Punggye-ri Nuclear Test Site is located in the northern part of DPRK with complicated land cover, high altitude and mountainous terrain. To achieve homogeneous and reliable measurements in the nuclear test site based on InSAR is really challenging. In mountainous regions, the atmospheric phase screen (APS) can cause serious problems in InSAR observation. From the images we have processed, it is obviously to distinguish atmospheric phase delay. Hence, we conduct APS correction based on WRF (Weather Research and Forecasting) and ECMWF (European Center for Medium range Weather Forecasting) to reduce the APS in D-InSAR processing. Second, the coherence of InSAR interferometric pairs is affected by many factors such as spatial-temporal baseline, wavelength and land cover. We selected multiple interferometric combinations and compared the performance of C-band Sentinel-1, L-band ALOS-2 and X-band TerraSAR-X in InSAR deformation monitoring. The results show that the L-band ALOS-2 data are generally more coherent therefore can provide effective information for surface deformation monitoring. Finally, due to the lack of external data to verify the reliability of InSAR results, we cross-validated the monitoring results of multi-source SAR data with different wavelengths, incident angles, and spatial resolutions aiming to get the robust and trustable result.

Key words：InSAR；Underground nuclear test；Surface deformations；Multiple SAR data；North Korea

Land cover classification is one of the important applications of polarimetric SAR (PolSAR) data. With the development of PolSAR techniques and the increasing demand for PolSAR data in applications, many SAR satellites with full-polarization mode have been successively launched, such as the widely used Japanese ALOS-2 PALSAR-2 (ALOS-2) and The Canadian RADARSAT-2 (R-2) data. China also successfully launched the first civilian SAR satellite with full-polarization in January 2017 - GF-3. However, due to the parameter differences in different SAR sensors, the resolution difference and difference in observation incidence, although in the same area there may be different land cover classification result obtained from different SAR images and the feature selection for classification may be different.

The aim of our study is to improve the land classification accuracy using GF-3, R-2 and ALOS-2 polarimetric SAR data. In this study, we used polarimetric decomposition results including Pauli decomposition H-α-A decomposition, and Yamaguchi decomposition as classification features and analyzed their distributions for different land cover types. After that, we selected the optimal combination of decomposition features as classification parameter for GF-3, R-2 and ALOS-2 respectively, and then carried out the experiments of land cover classification in Beijing. The results showed that for GF-3, using the components of Yamaguchi decomposition as feature parameters performs best, but for R-2 and ALOS-2, using the components of H-α-A decomposition as feature parameters performs best. Moreover, ALOS-2 has the highest classification accuracy (80%), but GF-3 and R-2 have similar classification accuracy (77%). Our study gives some references for the application of GF-3 PolSAR data.

Monitoring Anthropogenic Surface Deformation in Tibetan Plateau Using Sentinel-1 Data

Yunfeng Tian, Jingfa Zhang, Yi Luo, Yongsheng Li

Key Laboratory of Crustal Dynamics, Institute of Crustal Dynamics, China Earthquake Administration

1 Anningzhuang Road, Haidian, Beijing 100085, China

tel(O) 86-10-62842646 zhangjingfa@hotmail.com

1. INTRODUCTION

InSAR has been one of the key techniques for crustal deformation study. However, attentions should be paid to various nontectonic surface deformation which can also be captured by InSAR, for example, the ground subsidence due to extraction of underground water, which is common nowadays for densely populated urban regions. The presence of localized deformation arising from anthropogenic activities often obscures the movement of the Earth’s upper crust layer; and thus introduces bias in quantifying slip rates of active faults or motion of crustal blocks. In this work, we focus on the deformation related to human activities in Tibetan plateau, with the help the high-resolution Sentinel-1 C-band SAR data collected from late 2014 to early 2018, aiming to figure out various signals in the InSAR deformation map.

2. DATA & ANALYSIS

We used both ascending and descending orbital data of Sentinel-1 A/B satellites which serve as a validation of the signals we observed. The observation interval was 24 days from late 2014 to early 2017 and 12 days since middle 2017.

We processed the data using the GMTSAR software package (Sandwell et al., 2011). We first aligned all other acquisitions to the super master scene that we manually specified; and then generated interferograms for each acquisition pair. Strong decorrelation during the interferometric processing is rare due to the improved orbits of Senetinel-1 satellites and dry climate on the highland of Tibetan plateau, except for areas with strong seasonal frost deformation. The LOS displacement time series were generated using the coherence-based SBAS method which assigns small weights to pixels with lower coherence and produces a continuous deformation map, compared to traditional methods. Finally, the velocity was derived by fitting a straight line to the displacement time series.

3. RESULTS

(1). Ground subsidence due to mining

The Sentinel-1 data captured clearly the ground subsidence due to the mining activity at Zhaxikang (Figure 1), a town located right at the eastern fault trace of the Sangri-Cuona rift in southern Tibet. The maximum subsiding rate reaches ~10 mm/yr during the data period. Locations of construction sites and buildings were identified from the high-resolution multi-spectral images in Google Earth; and they were in good accordance with the distribution of the subsidence area in InSAR LOS rate map.

Figure 1. InSAR LOS rates (descending orbit) for Zhaxikang Mine in Sangri-Cuona Rift. (a) Location map. (b) Rate profile. The width of buffer zone is 5 km at both sides of the profile line. The color of symbol in profile plot represents the distance to the profile line.

(2). Ground uplift due to oil-drilling activity

There are several oil fields along the Mangya-Huatugou thrust fault zone in Qinghai province, China. The oil-drilling work usually involves injecting water down to the deep after extracting underground oil out, to maintain a certain level of pressure. We observed, using Sentinel-1 InSAR time series analysis, several localized uplifting areas in Qinghai province (Figure 2). The maximum uplifting rate can be > 10 mm/yr in the LOS direction.

Figure 2. InSAR LOS rates (descending orbit) for oil field north of Huatugou Town, Qinghai province, China. (a) Location map. (b) Rate profile.

(3) Other types of small-scale deformation or bias

The ground deformation can be also caused by other human activities, such as the extraction of underground water for agricultural irrigation or drinking in urban area. The cause of such subsidence can usually be investigated by checking the locations of villages or towns where high demand of water supply is often needed.

There are also some subsidence places where no obvious anthropogenic activities are presented. These regions often locate in the river basin or in valley between mountain peaks, and also along certain active fault zones. It is difficult to discern the cause of such deformation without help of other sources. Therefore, attentions should be paid when deriving the contemporary fault slip rate of such active fault.

Moreover, subsidence or uplift trend can also be fake deformation signal, especially in mountainous regions with high and steep topography. The situation might get worse, sometimes, in thrust faulting zone where both crustal uplifting and large topographic errors concur.

4. CONCLUSIONS

Our recent work using the latest spaceborne C-band SAR satellites (Sentinel-1 A/B) data demonstrated that InSAR technique nowadays is capable of measuring the crustal deformation at the millimeter level accuracy. Ground deformation related to anthropogenic activity, either subsidence or uplift, can be detected with sufficient confidence for the broad area in Tibetan plateau. However, there is also regional deformation whose origin is unknown or difficult to investigate. We should prefer to not make conclusions on geological issues before figuring out the origins of such observed deformation.

ACKNOWLEDGEMENTS

This work is supported jointly by National Science Foundation of China (41104001), China Earthquake Administration (Y201711), and Institute of Crustal Dynamics (ZDJ2017-29).

REFERENCES

Sandwell, D., R. Mellors, X. Tong, M. Wei, and P. Wessel (2011). Open radar interferometry software for mapping surface deformation, Eos Trans. AGU 92(28) 234, doi:10.1029/2011EO280002.

Landslides are one of the most damaging hazards for human beings and can be affected by multiple factors, including the natural environment and human activities. Since the Three Gorges Dam on the Yangtze River was completed in 2003, detecting and monitoring the landslides in the upstream area has become more important in order to protect human lives and properties. Compared to conventional in situ measurements, various remote sensing techniques have been carried out and found capable of monitoring landslides in difficult terrain over a large area.

This study focuses on monitoring landslides in the Three Gorges Region (TGR), which is characterised by the high humidity, dense vegetation, and steep slopes. Shuping with centre coordinates of 30.996◦N, 110.609◦E and Tanjiahe with centre coordinates of 31.030◦N, 110.509◦E are the two selected study sites. Synthetic aperture radar (SAR) techniques are applied to monitor landslides in these study areas and mitigation works performed to reduce the risks of landsldies in unstable areas. To assess the accuracy of digital elevation models (DEMs) derived from interferometric SAR data, TLS data was acquired by Zhang and co-workers and this is compared with the post-mitigation 6 m TDX CoSSC DEMs, SRTM and ASTER DEMs and DEMs derived from Cosmo-Skymed Spotlight data. The assessment of mitigation is also carried out by comparing two sets of Terrestrial Laser Scanning (TLS) data of the study sites before and after remediation.

The potential and limitations of using different SAR data, especially Sentinel-1 to identify unstable regions for follow-up acquisitions of TerraSAR-X Staring Spotlight and Cosmo-Skymed Spotlight data are described. The potential of TLS techniques which have not been widely used in previous studies will also be evaluated. Furthermore, the effect of mitigation in landslide area is also going to be assessed.

Acknowledgments: We thank Prof. J. Zhang, Dr. Q. Jiao, and Dr. T. Xue from the China Earthquake Administration for their support on our fieldwork.

Together with SAR interferometry (InSAR), Ground-Based Synthetic Aperture Radar (GBSAR) has proven to be a powerful field-based remote sensing tool for deformation monitoring. This work proposes two complete GBSAR data processing chains developed on the basis of advanced InSAR time series analysis algorithms including the Small Baseline Subset (SBAS) concept and the Persistent Scatterer Interferometry (PSI) for continuous deformation monitoring. The developed SBAS chain exploits redundant interferograms and processes consecutive GBSAR imagery unit by unit, which allows the opportunity to investigate temporarily coherent targets and reduces the requirement of computation memory. Contrarily, the PSI chain is more computationally sufficient and is developed to support early warning and rapid decision-making in urgent situations. Two practical applications are given in this work to demonstrate the feasibility of the developed GBSAR data processing chains for continuous deformation monitoring.

The magnitude 7.0 Jiuzhaigou earthquake occurred in August 8, 2017 resulted in a large number of landslides near the Jiuzhaigou panda sea, causing road congestion and seriously affecting the earthquake emergency rescue progress. The landslide caused by earthquake has the characteristics of wide distribution and large quantity. Because of the urgency of the disaster and high resolution of unmanned aerial vehicle (UAV) images the traditional artificial visual interpretation model cannot meet the needs of earthquake emergency response. Therefore, it is necessary to provide an automatic information identification method. Thus, the distribution range of landslide can be identified quickly and accurately.

Based on the deep analysis of the features of remote sensing images of landslide, an automatic information identification model for object oriented analysis is constructed. Firstly, the remote sensing images are segmented at different scales to obtain different levels of image objects according to different types and scales of land objects. Then, SEath algorithm is used to construct feature rule set automatically by comprehensive utilization of the information of spectrum, texture and shape of object at every level, and the distribution of earthquake-induced landslides is identified. After that, taking artificial visual interpretation as a reference, the recognition accuracy and efficiency are evaluated. Finally, the spatial distribution features of landslide body in topographic factor and fracture distribution layer are analyzed by statistical analysis. The overall accuracy is 94.8%, and the Kappa coefficient is 0.827. At the same time, on the basis of the same configuration of the computer, the present method is twice as efficient as that of the artificial visual interpretation method.

The paper also analyzes the earthquake-induced landslide distribution features in elevation, slope, aspect, fault distance and other factors. The correlation between landslide and topographic factors is found. It is concluded that the earthquake-induced landslide in the study area is mainly controlled by the Tazang fault. The spatial distribution rule can provide information support for landslide risk assessment, disaster investigation, prediction and prevention. There are obvious fault effects in the distribution of landslide.

The geologic observations presented above suggest that conjugate strike-slip faults are significant structures along the Bangong-Nujiang suture zone in central Tibet. However, some small fault zones located inside the Qinghai Xizang Plateau, especially in the secondary blocks, have not attracted enough attention. For example, a series of V-shaped conjugate strike slip fault systems between Lhasa block and Qiangtang block. The V-shaped conjugate strike slip fault zone is composed of a series of small fault zones with oblique lines. It is an important product of the neotectonic movement in the Qinghai Tibet Plateau. It plays an important role in the deformation of the East-West extensional tectonic deformation in the Qinghai Tibet Plateau. This study will use InSAR technology to obtain the surface deformation information of conjugate strike-slip faults(Bengco Fault and Dongqiao Fault). The two faults are nearly 300 km in length. Therefore, the wide range SAR data should be selected (for example, Sentinel-1 IW mode SAR width is 250km) and used to obtain the active fault deformation signal in the whole conjugate strike slip fault at one time, which will help the overall analysis of the fault distribution. We will analysis the whole motion characteristics of conjugate strike-slip faults，investigate the strain accumulation of tectonic deformation in time and space. It is helpful to understand the characteristics of a series of conjugate strike slip faults developed in the middle part of the Qinghai Tibet Plateau.

The tremendous development of InSAR missions (e.g., Sentinel-1A/1B, ALOS-2, TerraSAR-X/TanDEM-X, COSMO-SkyMED, RADARSAT-2, and Gaofen-3) in recent years facilitates the study of smaller amplitude geohazard deformation monitoring using longer time series and over greater spatial scale, and this trend is set to continue with Sentinel-1C/D, Gaofen-3B/C, RADARSAT Constellation planned for launch during 2018-2025. This poses more challenges for correcting interferograms for atmospheric (tropospheric) effects since the spatial and temporal variations of tropospheric delay may dominate over large scales and can cause errors comparable in magnitude to those associated with crustal deformation (e.g. landslides, city subsidence and so on). In previous attempts, observations from Global Navigation Satellite System (GNSS) and Numerical Weather Models (NWM) have been used to reduce atmospheric effects on InSAR measurements, but GNSS-based correction models are limited by the availability (and distribution) of GNSS stations, and for NWM-based correction models, there might be a time difference between NWM and radar observations.

To overcome this, we have developed a generic InSAR atmospheric correction model whose notable features comprise: (i) global coverage, (ii) all weather, all time useability, (iii) correction maps available in near real-time, and (iv) indicators to assess the correction performance and feasibility. The model integrates operational high resolution ECMWF data (0.125-degree grid, 137 vertical levels, 6-hour interval) and continuous GPS tropospheric delay estimates (every 5 minutes) using an iterative tropospheric decomposition model. The model’s performance for InSAR atmospheric correction was tested using globally-distributed interferograms, encompassing both flat and mountainous topographies, mid-latitude and near polar regions, monsoon and oceanic climate systems, achieving a phase precision and displacement accuracy of approximately 1 cm for the corrected interferograms. Indicators describing the model’s performance including (i) GPS network and ECMWF cross-RMS, (ii) phase-delay correlations, (iii) ECMWF time differences, and (iv) topography variations, were developed to provide quality control for subsequent automatic processing, and provide insights of the confidence level with which the generated atmospheric correction maps may be applied.

We have released the Generic Atmospheric Correction Online Service (GACOS) based on the proposed model (http://ceg-research.ncl.ac.uk/v2/gacos/). This service aims to provide InSAR atmospheric correction maps in a convenient way with all features discussed above. The website was released on 6th June 2017 and has received over 10 thousand requests from all over the world. Given the convenience and the real time availability, the website has rapidly responded to recent events such as the Maoxian Landslide (24 June 2017) and the Xinjiang Earthquake (8 August 2017) by providing the atmospheric corrections used in the generation of near real time deformation fields to identify surface damages and contribute to rescue and recovery operations, which have been reported and highlighted by over 20 social medias and organizations.

Seismic indirect economic loss assessment not only has a major impact on regional economic recovery policies, but also it is related to the economic assistance at the national level. However, due to the Cross-regional economic activities and the difficulty of obtaining data, the seismic indirect economic loss are often predicted based on the direct loss of buildings and life lines. Although this method takes into account the impact of production factor stock on economic flows, the effects of disasters on economic activity are neglected and the economic losses in the tertiary industry are seriously underestimated.

The Defense Meteorological Satellite Program (DMSP) provides global images of 4 periods which from morning to night. Since the Operational Linescan System of DMSP (DMSP-OLS) can observe the city night light, it was widely used in population distribution analysis, economic development monitoring and so on. This paper took Sichuan Province as an example to evaluate the impact of earthquake on economic activities on large spatial scale based on DMSP/OLS, and then estimated the recovery of the economy in the disaster area on the view of time and space by analyzing a series of data from pre-event 5 years to post-event 5 years. First, the county economic evaluation model is established. Upon image registration and correction, the nighttime light images are clipped by the county boundaries. Afterwards, counting the nightlight index of all counties, comparing with Sichuan Statistical yearbook, the corresponding relations between nightlight index and economic activities was finally established. Second, a seismic indirect loss Assessment method are presented. Through the analysis of the area and spatial distribution of night-time light around 2008, the spatial migration and change characteristics of economic activities were summarized, which were caused by Wenchuan earthquake. Then a functional relationship between seismic indirect economic loss and night-time light changes of post-earthquake was established. Third, the economic recovery of affected areas was evaluated. The economic recovery of Sichuan Province was evaluated in time and space by comparing with the cumulative growth of night-time light within the 5 years from 2009 to 2013 and the value of per-earthquake.

In this paper, more attention should be paid to the impact of earthquake on social economic activities. Especially in some areas dominated by the service industry, indirect economic losses can better reflect the impact of the disaster on the area. At the same time, it is also hoped that the application of night-time light data in the evaluation of earthquake disaster damage and restoration will also help the government to formulate a policy on regional economic assistance.

Abstract: In November 2017, a strong Mw7.3 earthquake occurred at the Iran Iraq border. The earthquake caused the surface to rise and settlement on both sides of the fault zone, and the maximum displacement of LOS was about 0.85m. The fault rupture begins in the northwest and continues along the fault to the southeast. The coseismic deformation field is retrieved based on ALOS-2 satellite data and Sentinel-1 satellite data. Using the two step inversion algorithm to do the seismic source mechanism inversion, the inversion results are compared with the USGS results and both of them have good coincidence degree, and the inversion of the seismic source mechanism is more fine. It can better analyze and describe the earthquake. The seismogenic structure laid the foundation for studying the fault structure in the area.

Keywords: Iran Iraq earthquake, D-InSAR, Seismic source mechanism inversion

1. research status

In November 12, 2017, a strong earthquake of magnitude Mw7.3 occurred on the Iraqi border in Iran. The epicenter was located at (34.886°N, 45.941° E) and the focal depth was 19km. The earthquake caused more than 500 deaths, thousands of injured, more than 7000 homeless and thousands of houses collapsing, causing huge economic losses and casualties to the local people.

The earthquake occurred at the front of the collision zone between the two large plates - the Arabia plate and the Eurasian plate, along the Iran and Iraq border in the northwest of the Zagros belt. The Zagros thrust belt is a long 1500km fold thrust belt which extends to the west of Iran and extends to northern Iraq. Although Iran and Iraq are earthquake prone areas, there has not been an earthquake above Mw5.0 for many years. The earthquake damage was relatively light on November 12 of 2017, because before the occurrence of the Mw7.3 earthquake, the region had 4.4 levels of pre-earthquake, and most of the people moved to the relatively safe area after the occurrence of the pre-earthquake.

After the earthquake, by collecting the SAR data before and after the earthquake, the coseismic deformation field can be analyzed and processed. Because the acquired SAR data can cover the focal area completely, so the differential interference measurement technique is used to deal with the very clear deformation field after the earthquake. Through the analysis of the coseismic deformation field, it can be seen that the earthquake caused a relative decline of the upper plate and uplifting of the footwall on both sides of the fault, and the maximum displacement of the satellite's flight direction is up to 0.85m.

The two step inversion algorithm is used to estimate the fracture set parameters and the slip distribution of the fault under the constraint of the InSAR result. Firstly, the fault is assumed to be a homogeneous fault model, and the geometric parameters of the fault are calculated. Then the distributed fault model is used to calculate the distributed slip on the fault surface. Using PSOKINV software to inverse the source parameters, the software uses an improved group cooperative stochastic search particle swarm optimization (Particle Swarm Optimization, PSO) algorithm, which mainly solves the optimal solution through a group of random solutions by iterative method.

2. research significance

The Iraq Iran border is located in the collision zone between the Arabia plate and the Eurasian continent plate. The energy of collisions is cumulative and released and then resulting the earthquakes. This area is a shallow source area at most time. Due to frequent devastating earthquake, the Iran government has formulated corresponding building regulations to ensure the safety of the lives and property of the residents. The earthquake magnitude is relatively large, but the casualties are relatively not very serious. It also indicates the necessity of the construction of earthquake resistant buildings and the study at the same time. The seismogenic background and fault structure of the area have important research significance for earthquake disaster prevention in this area.

One of the largest inland earthquakes in Taiwan happened on 21 September 1999, the Mw 7.6 Chi-Chi event. It struck the Taipei Basin, in the Central western part of the island, killing more than 2400 people and damaging 100 000 structures. The rupture was complex with several dislocations along the 100-km long Chelungpu thrust fault. An improved study of this earthquake will allow better understanding of regional fault properties.

Six ERS images from the descending track 232 and covering the period from 21 January 1999 to 25 May 2000 were processed to investigate the co-seismic deformation. The Interferometric Synthetic Aperture Radar (InSAR) technique was used and via the ESA open-source software SNAP. With InSAR, only the footwall can be analysed because the hanging-wall, which likely experiences the main deformation, is densely vegetated resulting in very low coherence in the interferograms. Co-seismic interferograms show about 10-11 fringes which is equivalent to a displacement variation of approximately 30 cm.

We used PSOKINV (Particle Swarm Optimization and Okada Inversion package), a geodetic inversion package, to determine the fault geometry and the slip distribution. First, the non-linear problem is to use the Particle Swarm Optimization (PSO) for geodetic modelling with the assumption of a uniform slip on a rectangular fault. Second, a joint inversion of InSAR and geodetic data (GNSS and levelling) is realised. The GNSS enables us to get information about the hanging-wall of the fault and to improve the modelling. The slip distribution is determined as a linear problem, optimally-smoothed parameters are obtained.

Sub-pixel offset tracking has been used in various applications, including measurements of volcanic activities, glacier movement, earthquakes, landslides, etc., as a complementary method to time series InSAR. In this work, we explore the use of a small baseline subset (SBAS) Offset Tracking approach to monitor very slow landslides with centimetre-level annual displacement rate, and in challenging
areas characterized by high humidity, dense vegetation cover, and steep slopes. This approach, herein referred to as SBAS Offset Tracking, is used to minimize temporal and spatial de-correlation in offset pairs, in order to achieve high density of reliable measurements. This approach is applied to a case study of the Tanjiahe landslide in the Three Gorges Region. Using the TerraSAR-X Staring Spotlight (TSX-ST) data. With sufficient point density, we estimate the precision of the SBAS offset
tracking approach to be 2–3 cm on average. The results are demonstrated accord well with corresponding GPS measurements.

The key point of earthquake emergency is to quickly grasp the disaster, that is, earthquake damage assessment, in which the seismic hazard assessment of buildings is closely related to human life and property, which is the main content of seismic hazard assessment. Bad weather will generally follow the earthquake, the polarization of synthetic aperture radar (PolSAR) which is an active microwave radiation source, can penetrate many materials such as the rain,clouds,fogs,etc, thus it can imaging for the disaster areas in all weather and in all time, withal, the acquisition of target polarization scattering characteristic is relevant to the shape and physical property of the ground target, which benefits to ground-object identification, therefore PolSAR is widely applied in earthquake emergency. Compared with early single-polarization and multi-polarization SAR, full polarization SAR obtain the best effect of observation through flexible change of polarization state, it gets more complete polarization information, more abundant measurement information data, stronger performance for features classification. Earthquake damage buildings extraction can be divided into two kinds of methods: using multi-temporal change detection method and single phase post-earthquake image extraction method. The former one does polarization target classification firstly, then constructs seismic difference map to extract the earthquake damage buildings. Its core is to construct the difference graph, common methods such as establishing the polarization likelihood ratio model, defining polarization difference degree through combining scattering difference and power difference, Whishart distance change detection method etc. There is a difference of scattering mechanism between the collapsed buildings and intact buildings in the fully polarimetric SAR image after the earthquake, which is the theoretical basis for the single phase post-earthquake image extraction.
The current methods include: Polarization classification combined with the minimum heterogeneity criteria aggregation of hierarchical clustering algorithm, and template matching based on feature of image retrieval, the introduction of polarization orientation Angle compensation mechanism to improve and complete the structure of the the collapsed buildings and intact buildings.

On 24^th June 2017, a catastrophic landslide suddenly buried the Xinmo village (in Sichuan province, south-western China), resulting in heavy causalities. After the failure, the disaster assessment was in urgent need for the rescue and relief work. Except the field observation or UAV, spaceborn SAR data could provide valuable information to the disaster assessment.

In this study, we proposed a method that used the SAR interferometry coherence map to identify the landslide boundary and source area. With use of Sentinel-1 SAR images acquired on 12^th, June 2017 and 24^th June, 2017 (13 hours after the failure), the landslide boundary and source area were mapped by this method. It was revealed that the source area of this landslide was not at the top of the mountain. Compared with the UAV image acquired on 26^th June 2017, the location of the landslide boundary and source area were consistent.

This results show that, this first Sentinel-1 interferogram, together with its corresponding coherence and amplitude maps, not only helped us identify the source area of this massive landslide, but also assisted with mapping the landslide boundary. Spaceborn SAR data could help the disaster assessment to some degree.

This study is aimed to studying electromagnetic anomalies before main shock and aftershocks of Jiuzhaigou earthquake (M=7.0, August 8, 2017) and comparing the phenomana observed by the ground-based CSELF network and by the SWARM satellites. The Jiuzhaigou earthquake (M=7.0, 13:19:46, August 8, 2017, UTC) occurred in the Sichuan province. The CSELF network consists of 30 stations across two main seismic belts in China, in which 15 stations are located in Sichuan and Yunnan provinces. Each station records five alternate EM filed components (Ex, Ey, Hx, Hy, Hz) in a frequency band of 0.001-1000Hz. The data have been recording for about 3 years using the network. In the study on the EM anomalies before earthquakes, the following steps are involved. The first step is to choose the quality data from huge amount of the observed data. Secondly Top-Down Level analysis is carried out for identifying and catching anomalies in the data based on the different time and different frequencies either for Network data or for SWARM data. The final step is to investigate the relationship of anomalies with earthquake events.

Through analysis on the huge amount of Network data, the time series from August 6^th to 12^th is good meaning on obvious disturbance noise existing in the data. But some anomalous phenomena appeared before main shock and successive 18 mid-strong aftershocks. Except for three aftershocks the anomalies are featured as (1) anomalous pulsating clustering of EM fields appeared simultaneously at several stations, e.g., at the station of JianGe in Sichuan, at the LiJiang and JingGu stations in Yunnan with 205km, 770km and 1110km distances to the epicenter, respectively. The smooth variation of EM fields appeared between adjacent clusterings. (2) The pulsating clustering started at about 12-13 minutes before the earthquake and lasted for about 10 minutes and recovered at about 3 minutes before the shock. (3) Individual pulse in the clustering has a period of about 60-80s. (4) The amplitude of maximum pulse in the clustering is about 70% higher than the background value of corresponding EM component. The anomalous pulses seem to be decreased with the distance to epicenter. The clustering form is similar to those of the Pc3-Pc5 pulse clustering, but the observed anomalies by SWARM appeared in the different time section. The clustering is also not caused by co-seismic waves (P and S waves). It is postulated that the anomalies before each shocks may be caused by the shocks during the process of earthquake generation.

Acknowledgement: Tang J, Chen X, Zhan Y, Xiao Q, etc. from IGCEA joined the CSELF observation. The study is supported by NDICC (15212Z0000001) and NSFC (41374077）.

Yaxin Bi¹, Vyron Christodoulou¹, George Wilkie¹, Zhao Guoze², Ming Huang¹ and Han Bing²

1) Faculty of Computing, Engineering and the Built Environment, University of Ulster, Co Antrim, United Kingdom

2) Institute of Geology, China Earthquake Administration, Beijing, China

Email: y.bi@ulster.ac.uk

Electromagnetic (EM) field is sensitive to the stress of plate tectonics, and changes in the duration of earthquake preparation, the changes would cause electromagnetic emission to transmit into ionosphere, which could be observed by satellites. There were a number of studies conducted on DEMETER satellite data, the results shown that precursory phenomena were captured before earthquakes. Piša D, et al. (2013) carried out a rigorous statistic analysis on the 8400 earthquakes that have a magnitude of 5 or greater than 5 and electromagnetic perturbations within 440 kilometers of the earthquake epicenters, the results revealed that the probability of electromagnetic attenuation was very high before 0-4 hours of the events. Le et al. (2015) conducted a survey on studies of ionospheric abnormal behaviors before some great earthquakes and reported ionospheric disturbance to different extent.

This study reports the progress of development of anomaly detection algorithms and their application to analysing the SWARM satellites data and discovering precursory phenomena before large earthquakes. The study selects three earthquakes, i.e. the Ludian earthquake with a magnitude 6.2 occurred on 3 August 2014 in Yunnan, China, the Peloponnese earthquake with a 5.7 magnitude occurred in southern Greece on 29 August 2014 and the Eketahuna earthquake with a 6.8 magnitude occurred in Peru on 20 January 2014. For each earthquake, a 1000kmx1000km study area is defined and divided into 9 grids. For each grid a time series data is generated, as a result each area has 9 sets of time series data. The duration of the selected data is from 25th March 2014 to 24 January 2015, which were recorded by the Vector Field Magnetometer (VFM).

Four different methods are used to generate time series data, i.e. first day, middle, predefined and average points in order to investigate artificial anomalies introduced when generating time series data. The three detection algorithms of CUSUM-EWMA, Fuzzy-inspired and Hot-SAX are specifically selected to address the unknown nature of the EM signals with respect to their duration, their amplitude and frequency changes, they are applied to analyse 27 sets of time series data in order to detect anomalous phenomena before these three earthquakes. The detected results show various phenomena, and no specific patterns can be discovered, which are closely related to the times of occurrence of these earthquakes. From this studying results, the interesting points are observed as follows:

• the algorithms are capable of detecting anomalies, the CUSUM-EWMA provides good anomaly detection, but it struggles in different anomaly cases.
• the satellites observe the whole earth, their revisit time and orbit reveal a serious constraint in generating sufficient and high quality time series data for earthquakes.
• difficulties appear in selecting the fuzzy membership functions (MF) that depend a lot on the form of the input signals

References:

1. Piša D, Němec F, Santolik O, et al. 2013. Additional attenuation of natural VLF electromagnetic waves observed by the DEMETER spacecraft resulting from preseismic activity. J Geophys Res, 118: 5286–5295.
2. Huijun Le, Jing Liu, Biqiang Zhao, Libo Liu. Recent progress in ionospheric earthquake precursor study in China: A brief review, Journal of Asian Earth Sciences. Volume 114, Part 2, Pages 420-430.

In this work we report the development of a system pipeline for the analysis of the Swam satellite electromagnetic data. Our objective is to provide a streamlined functional tool for analyzing electromagnetic data over regions and investigate the relationship of precursory electromagnetic signals to seismic events. The process of the system pipeline consists of three stages of data extraction, data pre-processing and anomaly detection. The first stage provides an interactive interface, allowing users to define study regions and periods of seismic events, and then extract data from the Swarm CDF data archive. The second stage consists of four different pre-processing methods, including the first arrival sampling within regions, middle points and average value, which address the data sparsity problem and the cause of artificial anomalies in a defined region. The last stage offers a range anomaly detection functions underpinned with a variant of the basic CUSUM-EWMA statistical algorithm, fuzzy-logics inspired method, and HOT-SAX method, etc. To demonstrate the potentials of the tool in applying different kinds of algorithms under an anomaly detection scope of electromagnetic sequential time series data, we select a seismic event under scrutiny is in Ludian, China and occurred on 03/08/2014, and present the usefulness of our approach and pinpoint some critical problems regarding satellite data that were identified.

With the support the Wireless Electro-Magnetic Method (WEM) project, we built the first Control Source Extremely Low Frequency (CSELF) continuous observation network which include 30 electromagnetic stations in Beijing Capital Area (BCA) and Southern Section of North-South Seismic Belt in China for the artificial and nature source singles recording. The instruments collect the data 16 seconds every ten minutes with sample rate of 256Hz and then the whole day’s data was analyzed with the method of Flourier transformation and the FFT length was set as 4096. After that we can get the spectrum with the frequency range from 3Hz to 48Hz and the Schumann resonance and six harmonic frequencies can be observed clearly, however, the peak frequency of Schumann resonance are slightly different due to the stations’ location and other factors.

By comparing the long-term observation data of the same station, we can see that 1.The annual variation of the spectrum in Schumann resonance frequency is basically the same as that of other frequency bands. the intensity of the magnetic field is strong in summer, low in winter and the law of long term change conforms to the half cycle sine wave form. From January to July, the power spectral density is increasing, while from July to December, the spectral density of the vibration amplitude decreases.2. The power spectrum of Schumann resonance frequency is smaller than that of surrounding frequency, that is, its variation is more concentrated. 3.For one station the peak frequency of Schumann resonance shift during time. Take Lijiang as an example, and the peak frequency of the first Schumann resonance frequency of the north to south magnetic field component in one year is between 7.5Hz and 7.9Hz, and tends to low frequency in winter and summer, and to high frequency in spring and autumn.